eVow and Jazzed review
Friday, October 29, 2010
Wednesday, October 27, 2010
IAC Q3 2010
Match is desperately buying traffic.
Singlesnet died.
IAC Personals has less than 2,000,000 subscribers.
The new IAC Q3 2010 says "Match" had nearly 1,818,000 paid subscribers but it really means [Match + Chemistry + NetClubEncuentro + Udate + PeopleMedia communities + SinglesNet + YahooPersonals].
I think it is a serious US Securities and Exchange Commission (SEC) fraud to use the word "Match" meaning IAC Personals.
Singlesnet died.
IAC Personals has less than 2,000,000 subscribers.
The new IAC Q3 2010 says "Match" had nearly 1,818,000 paid subscribers but it really means [Match + Chemistry + NetClubEncuentro + Udate + PeopleMedia communities + SinglesNet + YahooPersonals].
I think it is a serious US Securities and Exchange Commission (SEC) fraud to use the word "Match" meaning IAC Personals.
More about Personality-based Recommender Systems
"... Corpus of Face Video Clips with Affective and Personality Metadata"
"... The corpus presented here has been compiled for the needs of our research work of affective and personality based user modeling in recommender systems.
.......
We had 52 students of a secondary school who participated in the experiment. The average age was 18.3 years (SD = 0.56). There were 15 males and 37 females.
........
4.1. Affective content based recommender system
Part of the presented corpus was used to develop and evaluate an affective user and item modeling approach in a content based recommender (CBR) system.
........
4.2. Personality based collaborative recommender system
Another part of the presented corpus was used in an experiment where we evaluated a novel
personality based user similarity measure for collabrative filtering.
A well known drawback of collaborative filtering methods is the new user problem. It occurs when a new user joins the system and the system has little or no knowledge on the user’s preferences.
As the user similarity measures rely on explicit ratings given by the users, when there are little ratings available, the algorithm for finding similar users tend to give bad choices. Consecutively, the predicted ratings have low correlation with real ratings. In order to alleviate the new user problem we introduced an initial questionnaire to assess the personality of each user.
........
We constructed a user similarity measure as an Euclidian distance in the big five personality space.
(papers: Emotive and Personality Parameters in Multimedia Recommender Systems and Personality based user similarity measure for a collaborative recommender system)
.......
Statistical analysis showed that the proposed personality based user similarity measure yielded significantly better results than the rating based user similarity measure which makes it more suitable not only to alleviate the new user problem but also to use when the new user phase dies away.
........
4.3. Emotion detection from video clips
Part of our ongoing research work is the detection of emotion from face videoclips. Our goal is to develop a method for detecting emotions in users with two novel properties:
(i) the inclusion of personality parameters as features
and
(ii) detection in the valence-arousal-dominance (VAD) space (instead of the coarse space of basic
emotions).
........
The 2 WEAK POINTS are:
The Big 5 to assess personality of users
and
the age of the sample, they are too young persons (personality not consolidated yet)
Have you seen the next generation of recommender systems include normative personality traits?
That is the only way to improve recommender systems, to include the personality traits of their users.
Have you seen they need to calculate personality similarity between users?
Have you seen there are different formulas to calculate similarity?
In case you did not notice, recommender systems are morphing to .......... compatibility matching engines!!!
"... The corpus presented here has been compiled for the needs of our research work of affective and personality based user modeling in recommender systems.
.......
We had 52 students of a secondary school who participated in the experiment. The average age was 18.3 years (SD = 0.56). There were 15 males and 37 females.
........
4.1. Affective content based recommender system
Part of the presented corpus was used to develop and evaluate an affective user and item modeling approach in a content based recommender (CBR) system.
........
4.2. Personality based collaborative recommender system
Another part of the presented corpus was used in an experiment where we evaluated a novel
personality based user similarity measure for collabrative filtering.
A well known drawback of collaborative filtering methods is the new user problem. It occurs when a new user joins the system and the system has little or no knowledge on the user’s preferences.
As the user similarity measures rely on explicit ratings given by the users, when there are little ratings available, the algorithm for finding similar users tend to give bad choices. Consecutively, the predicted ratings have low correlation with real ratings. In order to alleviate the new user problem we introduced an initial questionnaire to assess the personality of each user.
........
We constructed a user similarity measure as an Euclidian distance in the big five personality space.
(papers: Emotive and Personality Parameters in Multimedia Recommender Systems and Personality based user similarity measure for a collaborative recommender system)
.......
Statistical analysis showed that the proposed personality based user similarity measure yielded significantly better results than the rating based user similarity measure which makes it more suitable not only to alleviate the new user problem but also to use when the new user phase dies away.
........
4.3. Emotion detection from video clips
Part of our ongoing research work is the detection of emotion from face videoclips. Our goal is to develop a method for detecting emotions in users with two novel properties:
(i) the inclusion of personality parameters as features
and
(ii) detection in the valence-arousal-dominance (VAD) space (instead of the coarse space of basic
emotions).
........
The 2 WEAK POINTS are:
The Big 5 to assess personality of users
and
the age of the sample, they are too young persons (personality not consolidated yet)
Have you seen the next generation of recommender systems include normative personality traits?
That is the only way to improve recommender systems, to include the personality traits of their users.
Have you seen they need to calculate personality similarity between users?
Have you seen there are different formulas to calculate similarity?
In case you did not notice, recommender systems are morphing to .......... compatibility matching engines!!!
Monday, October 25, 2010
eHarmony Brazil can not take off.
It was launched 2 months ago, it offers a 3 months free membership, but eHarmony Brazil can not take off.
ParPerfeito, one of the biggest competitors in Brazil, is in decadence since January 2009.
DivinoAmor, for religious persons, has no traffic.
Why eHarmony Brazil can not take off?
Lack of local offices, local representatives?
Lack of a big marketing campaign?
Who knows???
ParPerfeito, one of the biggest competitors in Brazil, is in decadence since January 2009.
DivinoAmor, for religious persons, has no traffic.
Why eHarmony Brazil can not take off?
Lack of local offices, local representatives?
Lack of a big marketing campaign?
Who knows???
Thursday, October 21, 2010
The NEW era of Personality Based Recommender Systems
Personality Based Recommender Systems include personality traits of their users and need to calculate personality similarity between them in order to make predictions (to recommend products or services)
Personality Based Recommender Systems perform better than Behavioural ones.
Personality Based Recommender Systems assess personality traits of their users in different ways, they can use questionnaires of quizzes, and then they need to calculate personality similarity.
There are several formulas to calculate similarity.
I have discussed some of them in previous posts:
Personality Based Recommender Systems
The PLAGUE of recommender systems
Recommender Systems and the Social Web
In these papers, they propose a NEW formula to calculate similarity
"Addressing the New User Problem with a Personality Based User Similarity Measure"
"Personality Based User Similarity Measure for a Collaborative Recommender System"
"We propose a novel approach for calculating the user similarity for collaborative filtering recommender systems that is based on the big five personality model .... we focus on memory-based (collaborative filtering recommenders) CF systems. CF recommenders are based on the presumption that when the similarity between two users is high both users will like similar items. The similarity measure is thus a crucial part of any CF system.
We propose to use a similarity measure that yields, for each user u, a list of close neighbours that have in common a similar emotive response pattern to content items. (it lowers the impact of the sparsity problem as the calculation of similarities does not depend on ratings.)
We evaluated the CF using three different user similarity measures:
(i) a standard, rating based measure (equation 8),
(ii) an Euclidian big five based measure (equation 9)
and
(iii) weighted Euclidian big five based measure (equation 10).
Focusing on the relevant items a classifier yields four groups:
(i) true positives (TP) are items that are relevant to the user and have been correctly classified as relevant,
(ii) true negatives (TN) are items that have been correctly classified as non relevant,
(iii) false positives (FP) are items that are non-relevant but have been misclassified as relevant
and (iv) false negatives (FN) are items that are relevant but have been misclassified as non relevant.
Precision P is the rate of truly relevant items among all the items classified as relevant by the CF system (equation 12)
The F measure combines precision and recall in a single numerical value (equation 14)
We performed an offline experiment of a memory based CF recommender system that relies on end users' personality parameters to determine the nearest neighbours, which is a crucial part of the recommending procedure. We compared four personality based similarity measures and two rating based similarity measures. The CF recommender system's performance results showed that the personality based measures were statistically equivalent or superior (the mean value of F was significantly higher) to the rating based measure.
In terms of mean values of P, R and F the big five based approaches performed better than the ratings based approaches. Table 4 & Figure 3 "
but guess, it is useless at all for serious dating proposals!!! It is not going to outperform actual compatibility Matching Methods based on personality profiling.
It will reach as low as 3 to 4 persons recommended -on average- per 1,000 persons screened, the same range as searching by your own, with high false positives.
You can also read:
"Improving the believability in the interaction of synthetic virtual agents: Towards Personality in Group Dynamics"
"Recommender System based on Personality Traits" 142 pages (Uses Big5)
page 24 describes Dating Systems
page 52
3.2.2 Modelling the prototype
In order to generate an effective recommendation, we propose a prototype of a Recommender System that is able to match users' similarities* (or dissimilarities) in Personality Traits.
*Those similarities could be measured considering the fine-grained (6 facets per each Big five dimension) or the coarse-grained (only the 5 dimensions of the Big Five) traits. Indeed, similarities may be measured considering a singular Personality Trait.
page 54
3.2.2.4 Similarity Function
The Similarity Function is in charge of computing the general ranking of facets or dimensions from vectors-ranking. The ranking is projected according to the Decision Module, and
this means that the decision process is projected by the designer of the Recommender System according to the set of traits that should be considered for the recommendation by similarity/
dissimilarity.
3.2.3 Approaches and Techniques
The prototype of our Recommender System was implemented according to the Nearest Neighbor approach. The Nearest Neighbor is an approach used in the Recommender System to optimize the problem of finding closest items to recommend.
(The matching was based on similarity of Personality Traits and the technique applied was the nearest neighbor.)
page 77
Other Scenarios for application of Recommender Systems
We present some other scenarios where we can apply Recommender Systems based on Psychological Traits.
Recommending soul-mate in Dating Systems
Recommender Systems based on Personality Traits applied to Dating Systems might be an
alternative for people who search for a compatible romantic mate. Dating Systems which use
psychological aspects to search for compatibility in the recommendations are more likely to
generate successful couples than traditional ones."
Book
"Recommender Systems based on Personality Traits:Could human psychological aspects influence the computer decision-making process?"
---------------------------------
"A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem" (DOES NOT include personality traits)
"Collaborative filtering is one of the most successful and widely used methods of automated product recommendation in online stores. The most critical component of the method is the mechanism of finding similarities among users using product ratings data so that products can be recommended based on the similarities. The calculation of similarities has relied on traditional distance and vector similarity measures such as Pearson's correlation and cosine which, however, have been seldom questioned in terms of their effectiveness in the recommendation problem domain. This paper presents a new heuristic similarity measure"
Personality Based Recommender Systems perform better than Behavioural ones.
Personality Based Recommender Systems assess personality traits of their users in different ways, they can use questionnaires of quizzes, and then they need to calculate personality similarity.
There are several formulas to calculate similarity.
I have discussed some of them in previous posts:
Personality Based Recommender Systems
The PLAGUE of recommender systems
Recommender Systems and the Social Web
In these papers, they propose a NEW formula to calculate similarity
"Addressing the New User Problem with a Personality Based User Similarity Measure"
"Personality Based User Similarity Measure for a Collaborative Recommender System"
"We propose a novel approach for calculating the user similarity for collaborative filtering recommender systems that is based on the big five personality model .... we focus on memory-based (collaborative filtering recommenders) CF systems. CF recommenders are based on the presumption that when the similarity between two users is high both users will like similar items. The similarity measure is thus a crucial part of any CF system.
We propose to use a similarity measure that yields, for each user u, a list of close neighbours that have in common a similar emotive response pattern to content items. (it lowers the impact of the sparsity problem as the calculation of similarities does not depend on ratings.)
We evaluated the CF using three different user similarity measures:
(i) a standard, rating based measure (equation 8),
(ii) an Euclidian big five based measure (equation 9)
and
(iii) weighted Euclidian big five based measure (equation 10).
Focusing on the relevant items a classifier yields four groups:
(i) true positives (TP) are items that are relevant to the user and have been correctly classified as relevant,
(ii) true negatives (TN) are items that have been correctly classified as non relevant,
(iii) false positives (FP) are items that are non-relevant but have been misclassified as relevant
and (iv) false negatives (FN) are items that are relevant but have been misclassified as non relevant.
Precision P is the rate of truly relevant items among all the items classified as relevant by the CF system (equation 12)
The F measure combines precision and recall in a single numerical value (equation 14)
We performed an offline experiment of a memory based CF recommender system that relies on end users' personality parameters to determine the nearest neighbours, which is a crucial part of the recommending procedure. We compared four personality based similarity measures and two rating based similarity measures. The CF recommender system's performance results showed that the personality based measures were statistically equivalent or superior (the mean value of F was significantly higher) to the rating based measure.
In terms of mean values of P, R and F the big five based approaches performed better than the ratings based approaches. Table 4 & Figure 3 "
but guess, it is useless at all for serious dating proposals!!! It is not going to outperform actual compatibility Matching Methods based on personality profiling.
It will reach as low as 3 to 4 persons recommended -on average- per 1,000 persons screened, the same range as searching by your own, with high false positives.
You can also read:
"Improving the believability in the interaction of synthetic virtual agents: Towards Personality in Group Dynamics"
"Recommender System based on Personality Traits" 142 pages (Uses Big5)
page 24 describes Dating Systems
page 52
3.2.2 Modelling the prototype
In order to generate an effective recommendation, we propose a prototype of a Recommender System that is able to match users' similarities* (or dissimilarities) in Personality Traits.
*Those similarities could be measured considering the fine-grained (6 facets per each Big five dimension) or the coarse-grained (only the 5 dimensions of the Big Five) traits. Indeed, similarities may be measured considering a singular Personality Trait.
page 54
3.2.2.4 Similarity Function
The Similarity Function is in charge of computing the general ranking of facets or dimensions from vectors-ranking. The ranking is projected according to the Decision Module, and
this means that the decision process is projected by the designer of the Recommender System according to the set of traits that should be considered for the recommendation by similarity/
dissimilarity.
3.2.3 Approaches and Techniques
The prototype of our Recommender System was implemented according to the Nearest Neighbor approach. The Nearest Neighbor is an approach used in the Recommender System to optimize the problem of finding closest items to recommend.
(The matching was based on similarity of Personality Traits and the technique applied was the nearest neighbor.)
page 77
Other Scenarios for application of Recommender Systems
We present some other scenarios where we can apply Recommender Systems based on Psychological Traits.
Recommending soul-mate in Dating Systems
Recommender Systems based on Personality Traits applied to Dating Systems might be an
alternative for people who search for a compatible romantic mate. Dating Systems which use
psychological aspects to search for compatibility in the recommendations are more likely to
generate successful couples than traditional ones."
Book
"Recommender Systems based on Personality Traits:Could human psychological aspects influence the computer decision-making process?"
---------------------------------
"A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem" (DOES NOT include personality traits)
"Collaborative filtering is one of the most successful and widely used methods of automated product recommendation in online stores. The most critical component of the method is the mechanism of finding similarities among users using product ratings data so that products can be recommended based on the similarities. The calculation of similarities has relied on traditional distance and vector similarity measures such as Pearson's correlation and cosine which, however, have been seldom questioned in terms of their effectiveness in the recommendation problem domain. This paper presents a new heuristic similarity measure"
Saturday, October 16, 2010
Hormonal birth control use and relationship jealousy
FRESH and NEW paper
Cobey, K. D., et al. "Hormonal birth control use and relationship jealousy: Evidence for estrogen dosage effects." Personality and Individual Differences (2010), doi:10.1016/j.paid.2010.09.012
".... this study supplements the existing literature which suggests that hormonal oral contraceptive pill use may influence female mate choice preferences and relationship dynamics. For example, evidence indicates that, relative to non-pill users, women on Combined oral contraceptives (COC) show no or weaker preferences for masculine faces and voices and a decreased preference for genetic dissimilarity in partners. It may be that pill-associated changes in preferences for masculinity and genetic dissimilarity are mediated not just by the absence of an estrus phase but also by COC ethinyl estradiol and progestin concentrations."
Please also read the post "Innovations: to take the 16PF5 test 3 times"
I think women will love a rational explanation of why they need to take the test 3 times.
A Marketing campaign should be incisive in this new discovery:
contraceptive hormonal methods alter mate choice in humans because only short-term but not long-term partner preferences tend to vary with the menstrual cycle.
Cobey, K. D., et al. "Hormonal birth control use and relationship jealousy: Evidence for estrogen dosage effects." Personality and Individual Differences (2010), doi:10.1016/j.paid.2010.09.012
".... this study supplements the existing literature which suggests that hormonal oral contraceptive pill use may influence female mate choice preferences and relationship dynamics. For example, evidence indicates that, relative to non-pill users, women on Combined oral contraceptives (COC) show no or weaker preferences for masculine faces and voices and a decreased preference for genetic dissimilarity in partners. It may be that pill-associated changes in preferences for masculinity and genetic dissimilarity are mediated not just by the absence of an estrus phase but also by COC ethinyl estradiol and progestin concentrations."
Please also read the post "Innovations: to take the 16PF5 test 3 times"
I think women will love a rational explanation of why they need to take the test 3 times.
A Marketing campaign should be incisive in this new discovery:
contraceptive hormonal methods alter mate choice in humans because only short-term but not long-term partner preferences tend to vary with the menstrual cycle.
Thursday, October 14, 2010
AmorEnLinea & OasisActive
There 2 FREE online dating sites (1.0 only search engines) getting some traction here, in some Latin American countries (Venezuela, Perú, México, Colombia, Chile and Argentina but NOT Brazil). The Spanish translation is excellent.
They share their databases, OasisActive and AmorEnLinea, both owned by the Australian 3H Group (the former owners of Match).
Those sites are 100% free, ads supported only.
3h Group also owns TataDate for the Chinese market.
It is quite intriguing, they are full of profiles of old persons (over 60 years old)
Those profiles seem to be REAL.
Here some screenshots:
They share their databases, OasisActive and AmorEnLinea, both owned by the Australian 3H Group (the former owners of Match).
Those sites are 100% free, ads supported only.
3h Group also owns TataDate for the Chinese market.
It is quite intriguing, they are full of profiles of old persons (over 60 years old)
Those profiles seem to be REAL.
Here some screenshots:
Wednesday, October 13, 2010
Online Dating in Argentina
Here is a fresh link of an article about Online Dating in Argentina.
http://www.lanacion.com.ar/nota.asp?nota_id=1312612
ZonaCitas from La Nación Newspaper
and
Match
Soon I will be reviewing OasisActive. It has some traction in Latin American Countries.
http://www.lanacion.com.ar/nota.asp?nota_id=1312612
ZonaCitas from La Nación Newspaper
and
Match
Soon I will be reviewing OasisActive. It has some traction in Latin American Countries.
Personality-based Recommender System
Have you seen the next generation of recommender systems are going to include personality traits?
"Design and User Issues in Personality-based Recommender Systems"
Abstract
"Recommender systems have emerged as an intelligent information filtering tool to help users effectively identify information items of interest from an overwhelming set of choices and provide personalized services. Studies show that personality influences human decision making process and interests. However, little research has ventured in incorporating it into recommender systems. ... The overall goal is to develop an efficient personality-based recommender system and to arrive at a series of design guidelines from the perspective of human computer interaction"
In that paper, the results on a proposed personality-based music recommender prototype is presented
[ That Personality-based Recommender System is useless at all for serious dating proposals.
That paper calculates personality similarity between users: ".. we treat users' personality characteristics as a vector as rating records. For each user u, his/her personality descriptor Pu is a n-dimension vector. Consequently, the similarity between two user u and v can be computed as the Pearson correlation coefficient of their personality descriptors..."
but think for a minute! If you use those Personality-based Recommender Systems for Online Dating Purposes, then they are ... guess ... Compatibility Matching Algorithms!
If they use the Big5 to assess personality and the Pearson correlation coefficient to calculate similarity, they are nothing new, the same stuff already available.
The Big 5 traits personality model is good for orientative purposes but not good enough for predictive purposes.
"Because the Big Five groups the more specific primary-level factors, feedback organized around the five Global Factor scales is more easily understood. For detailed feedback or predictive purposes, one should assess the more specific primary factors. Research has shown that more specific factors like the primary scales of the 16PF Questionnaire predict actual behavior better than the Big 5 Global Factors. For example, one extravert (a bold, fearless, high-energy type) may differ considerably from another (a sweet, warm, sensitive type), depending on the extraversion-related primary scale score patterns, so deeper analysis is typically warranted."
Extracted from the 16PF5 Manual
Moreover if a visual personality quizz is used to assess personality of users, like the one offered by VisualDNA or Dewey Color System, it adds a lot of distortion to the measurement. Those Personality-based Recommender Systems will perform worse than actual Compatibility Matching Algorithms!
]
"Design and User Issues in Personality-based Recommender Systems" is related to
"Using Personality Information in Collaborative Filtering for New Users"
(they) compare the performances of rating-based similarity (RBS), personality-based similarity (PBS) and their hybrid (RPBS) in different start-up settings
Figure 1 shows how RPBS and PBS are better models than RBS (in a sparse music dataset).
Tuesday, October 12, 2010
"The Future of an Applied Evolutionary Psychology for Human Partnerships"
"The Future of an Applied Evolutionary Psychology for Human Partnerships"
Review of General Psychology (in press).
Abstract
There has been significant recent progress in our understanding of human mate choice. We outline several frontiers of rapid cultural change which may increasingly directly affect individual selfevaluation in the mating market, formation and maintenance of long-term partnerships, and potentially reproductive outcome and child health. Specifically, we review evidence for the effects of (1) increasing exposure to mass media, (2) the advent of novel ways to meet potential partners, and (3) cultural influences which may disrupt or alter the expression of evolved mate preferences. We comment on the potential for these effects to influence self-perception and partner-perception, with downstream effects on relationship satisfaction and stability. ....
1) Effects of mass media on self-perception and relationship dissatisfaction.
Mass media (films, magazines, etc.) present unrealistic distributions of desirable others. Recent research demonstrates that media exposure can have negative consequences for selfassessments, partner-assessments, relationship satisfaction, mental health, body image, and relationship behaviors.... The combined effects of increases in divorce, serial dating, depression and anorexia may contribute to great societal change.
2) Globalisation, isolation and modern mating markets.
.. it seems likely that innovations such as personal advertisements and speeddating may tend to emphasise short-term judgements and the importance of physical attributes over gradual relationship building and the importance of complementary emotional and personality characteristics.
3) Disruption of evolved preferences.
- Cultural influences and evolved preferences: One example of an effect of cultural change on underlying preferences is the use of hormonally-based contraception and its apparent consequences on a user's behavior. ... Several studies show that pill users make different mate choices, on average, compared to non-users .... pill users show similar preferences to those of pregnant women.
Perhaps the most-widely reported difference attributed to pill use is that of women's
preference for the body odour of men who share a relatively high proportion of genes at a specific genetic region known as the Major Histocompatibility Complex, or MHC.
non-users preferred odours of MHC-dissimilar men, as found in other animals, but that pill users displayed the opposite trend.
Please also see:
1) "Human oestrus" Gangestad & Thornhill (2008)
"Only short-term but not long-term partner preferences tend to vary with the menstrual cycle"
2) "Does the contraceptive pill alter mate choice in humans?" Alvergne & Lummaa (2009)
".. whereas normally cycling women express a preference for MHC (Major Histocompatibility Complex) dissimilarity in mates, pill users prefer odours of MHC-SIMILAR men, indicating that pill use might eliminate adaptive preferences for genetic dissimilarity."
Review of General Psychology (in press).
Abstract
There has been significant recent progress in our understanding of human mate choice. We outline several frontiers of rapid cultural change which may increasingly directly affect individual selfevaluation in the mating market, formation and maintenance of long-term partnerships, and potentially reproductive outcome and child health. Specifically, we review evidence for the effects of (1) increasing exposure to mass media, (2) the advent of novel ways to meet potential partners, and (3) cultural influences which may disrupt or alter the expression of evolved mate preferences. We comment on the potential for these effects to influence self-perception and partner-perception, with downstream effects on relationship satisfaction and stability. ....
1) Effects of mass media on self-perception and relationship dissatisfaction.
Mass media (films, magazines, etc.) present unrealistic distributions of desirable others. Recent research demonstrates that media exposure can have negative consequences for selfassessments, partner-assessments, relationship satisfaction, mental health, body image, and relationship behaviors.... The combined effects of increases in divorce, serial dating, depression and anorexia may contribute to great societal change.
2) Globalisation, isolation and modern mating markets.
.. it seems likely that innovations such as personal advertisements and speeddating may tend to emphasise short-term judgements and the importance of physical attributes over gradual relationship building and the importance of complementary emotional and personality characteristics.
3) Disruption of evolved preferences.
- Cultural influences and evolved preferences: One example of an effect of cultural change on underlying preferences is the use of hormonally-based contraception and its apparent consequences on a user's behavior. ... Several studies show that pill users make different mate choices, on average, compared to non-users .... pill users show similar preferences to those of pregnant women.
Perhaps the most-widely reported difference attributed to pill use is that of women's
preference for the body odour of men who share a relatively high proportion of genes at a specific genetic region known as the Major Histocompatibility Complex, or MHC.
non-users preferred odours of MHC-dissimilar men, as found in other animals, but that pill users displayed the opposite trend.
Please also see:
1) "Human oestrus" Gangestad & Thornhill (2008)
"Only short-term but not long-term partner preferences tend to vary with the menstrual cycle"
2) "Does the contraceptive pill alter mate choice in humans?" Alvergne & Lummaa (2009)
".. whereas normally cycling women express a preference for MHC (Major Histocompatibility Complex) dissimilarity in mates, pill users prefer odours of MHC-SIMILAR men, indicating that pill use might eliminate adaptive preferences for genetic dissimilarity."
Friday, October 8, 2010
The PLAGUE of recommender systems for the Online Dating Industry
I strongly agree with the paper
"A Survey of Accuracy Evaluation Metrics of Recommendation Tasks"
when it says
" ... consider a recommender system for an online dating site. Precision describes what proportion of the suggested pairings for a user result in matches. The false positive rate describes what proportion of unsuitable candidates are paired with the active user. Since presenting unsuitable candidates can be especially undesirable in this setting, the false positive rate could be the most important factor." page 2945
The first recommender system (for the Online Dating Industry) I saw in a paper was:
"Recommender System for Online Dating Service (2007)"
then I saw the one offered by IntroAnalytics.
I had asked IntroAnalytics about the range of its algorithm.
NOW is coming "The PLAGUE of recommender systems for the Online Dating Industry"
"Reciprocal Recommender System for Online Dating" final version
"Reciprocal Recommender System for Online Dating" demo
"Learning User Preferences in Online Dating"
"AI Dating: Development of a Novel Dating Application with Fuzzy Inferencing"
Those NEW recommender systems are only RUBBISH, useless at all for serious dating proposals.
Many recommender systems do not take into account the discovery uncovered by Eastwick and Finkel 2008; also Kurzban and Weeden, 2007; Todd, Penke, Fasolo, and Lenton, 2007 who found that people often report partner preferences that are not compatible with their choices in real life.
Some online dating sites had been using Behavioural Bidirectional Recommendation Engines for years, like PlentyOfFish, and they could not outperform compatibility Matching Methods based on personality profiling.
There is a range convergence phenomenon between the 3 mains tools online dating sites can offer: searching by your own, Bidirectional Recommendation Engines and Compatibility Matching Methods. Any member receives on average 3 or 4 prospective mates as selected / recommended / compatible for dating purposes per 1,000 (one thousand) members screened in the database.
They all 3 are performing the same for serious daters, with a high percentage of false positives, like gun machines shooting flowers.
That range convergence phenomenon is what I had called "the online dating sound barrier", in 2003, when I had discovered than problem, 7 long years ago.
Latest Research in Theories of Romantic Relationships Development, which outlines: compatibility is all about a high level on personality* similarity* between prospective mates for long term mating with commitment.
*personality measured with a normative test.
*similarity: there are different ways to calculate similarity, it depends on how mathematically is defined.
Without offering the 16PF5 (or similar test measuring exactly the 16 personality factors) for serious dating, it will be impossible to innovate and revolutionize the Online Dating Industry.
"A Survey of Accuracy Evaluation Metrics of Recommendation Tasks"
when it says
" ... consider a recommender system for an online dating site. Precision describes what proportion of the suggested pairings for a user result in matches. The false positive rate describes what proportion of unsuitable candidates are paired with the active user. Since presenting unsuitable candidates can be especially undesirable in this setting, the false positive rate could be the most important factor." page 2945
The first recommender system (for the Online Dating Industry) I saw in a paper was:
"Recommender System for Online Dating Service (2007)"
then I saw the one offered by IntroAnalytics.
I had asked IntroAnalytics about the range of its algorithm.
NOW is coming "The PLAGUE of recommender systems for the Online Dating Industry"
"Reciprocal Recommender System for Online Dating" final version
"Reciprocal Recommender System for Online Dating" demo
"Learning User Preferences in Online Dating"
"AI Dating: Development of a Novel Dating Application with Fuzzy Inferencing"
Those NEW recommender systems are only RUBBISH, useless at all for serious dating proposals.
Many recommender systems do not take into account the discovery uncovered by Eastwick and Finkel 2008; also Kurzban and Weeden, 2007; Todd, Penke, Fasolo, and Lenton, 2007 who found that people often report partner preferences that are not compatible with their choices in real life.
Some online dating sites had been using Behavioural Bidirectional Recommendation Engines for years, like PlentyOfFish, and they could not outperform compatibility Matching Methods based on personality profiling.
There is a range convergence phenomenon between the 3 mains tools online dating sites can offer: searching by your own, Bidirectional Recommendation Engines and Compatibility Matching Methods. Any member receives on average 3 or 4 prospective mates as selected / recommended / compatible for dating purposes per 1,000 (one thousand) members screened in the database.
They all 3 are performing the same for serious daters, with a high percentage of false positives, like gun machines shooting flowers.
That range convergence phenomenon is what I had called "the online dating sound barrier", in 2003, when I had discovered than problem, 7 long years ago.
Latest Research in Theories of Romantic Relationships Development, which outlines: compatibility is all about a high level on personality* similarity* between prospective mates for long term mating with commitment.
*personality measured with a normative test.
*similarity: there are different ways to calculate similarity, it depends on how mathematically is defined.
Without offering the 16PF5 (or similar test measuring exactly the 16 personality factors) for serious dating, it will be impossible to innovate and revolutionize the Online Dating Industry.
Thursday, October 7, 2010
Recommender Systems and the Social Web
"Recommender Systems and the Social Web"
September 26, 2010. It includes all these papers:
- "Credibility-aware Web-based Social Network Recommender: Follow the Leader"
".. credibility based clustering derived from the Follow_the_Leader model provides a highly effective approach to identify Top-N recommenders, who are leaders in the context with the highest trustworthiness and expertise among all users." equation (11) outlined in section 4.3, replacing similarity weight in [Massa, P. and Avesani, P., Trust-aware recommender systems. in, (2007), ACM, 24.] to credibility weight.
- "Augmenting Online Video Recommendations by Fusing Review Sentiment"
- "Using Personality Information in Collaborative Filtering for New Users" (MUST READ PAPER)
".. we propose a method that combines human personality characteristics into the traditional rating-based similarity computation in the framework of user-based collaborative filtering systems with the motivation to make good recommendations for new users who have rated few items." ".. The most commonly used similarity calculation method is Pearson correlation coefficient ... We adopt [ a ] modified correlation-based similarity score in our evaluation experiment .." we compare the performances of rating-based similarity (RBS), personality-based similarity (PBS) and their hybrid (RPBS) in different start-up settings." Figure 1 shows how RPBS and PBS are better models than RBS (in a sparse music dataset)
- "Rating items by rating tags"
- "Improving Link Analysis for Tag Recommendation in Folksonomies"
- "Towards Understanding the Challenges Facing Effective Trust-Aware Recommendation"
- "Toward a Design Space for the Recommendation of Communities"
- "Collaboration and Reputation in Social Web Search"
- "Niche Trend Search for Recommender System based on Knowledgeable Blogger Group"
- "Resource Recommendation for Social Tagging: A Multi-Channel Hybrid Approach"
- "Weighted Content Based Methods for Recommending Connections in Online Social Networks"
HAVE YOU SEEN how the word SIMILARITY has different meanings for different persons or companies?
DO NOT use or DO NOT try to adapt ANY of the FORMULAS suggested by the above papers (for serious online dating proposals) because your Online Dating Site will reach "as low as" 3 to 4 persons high compatible per 1,000 persons screened, so in a 1,000,000 women database, any man will see as many as 3,000 to 4,000 women to contact (nearly at the same time), that means, a whole precision LESS than anyone could achieve by searching on one's own!
In previous posts I had analyzed:
"Personality Similarity calculation paper: A Novel K-Means Based Clustering Algorithm for High Dimensional Data Sets"
and
"Recommender System for Online Dating Service"
If you want to see how to solve that problem, please see the post
"the online dating sound barrier"
The Online Dating Industry for serious daters DOES NOT need to be more social. It needs to be more effective.
The Online Dating Industry for serious daters does not need a 10% improvement, a 50% improvement or a 100% improvement.
It does need "a 100 times better improvement"
Breaking "the online dating sound barrier" is to achieve at least:
3 most compatible persons in a 100,000 persons database.
12 most compatible persons in a 1,000,000 persons database.
48 most compatible persons in a 10,000,000 persons database.
100 times better than Compatibility Matching Algorithms used by actual online dating sites!
The only way to achieve that is:
- using the 16PF5 normative personality test, available in different languages to assess personality of members, or a proprietary test with exactly the same traits of the 16PF5. The ensemble of the 16PF5 is: 10E16, big number as All World Population is nearly 6.7 * 10E9
(WorldWide, there are over 5,000 -five thousand- online dating sites, but no one is using the 16PF5)
- expressing compatibility with eight decimals, like The pattern 6.7.6.8.9.6.7.7.8.7.2.5.8.7.3.4 is 92.55033557% +/- 0.00000001% similar to the pattern 7.7.6.8.8.7.6.5.8.7.4.5.7.7.3.4
Using a quantized pattern comparison method (part of pattern recognition by cross-correlation) to calculate similarity between prospective mates.
September 26, 2010. It includes all these papers:
- "Credibility-aware Web-based Social Network Recommender: Follow the Leader"
".. credibility based clustering derived from the Follow_the_Leader model provides a highly effective approach to identify Top-N recommenders, who are leaders in the context with the highest trustworthiness and expertise among all users." equation (11) outlined in section 4.3, replacing similarity weight in [Massa, P. and Avesani, P., Trust-aware recommender systems. in, (2007), ACM, 24.] to credibility weight.
- "Augmenting Online Video Recommendations by Fusing Review Sentiment"
- "Using Personality Information in Collaborative Filtering for New Users" (MUST READ PAPER)
".. we propose a method that combines human personality characteristics into the traditional rating-based similarity computation in the framework of user-based collaborative filtering systems with the motivation to make good recommendations for new users who have rated few items." ".. The most commonly used similarity calculation method is Pearson correlation coefficient ... We adopt [ a ] modified correlation-based similarity score in our evaluation experiment .." we compare the performances of rating-based similarity (RBS), personality-based similarity (PBS) and their hybrid (RPBS) in different start-up settings." Figure 1 shows how RPBS and PBS are better models than RBS (in a sparse music dataset)
- "Rating items by rating tags"
- "Improving Link Analysis for Tag Recommendation in Folksonomies"
- "Towards Understanding the Challenges Facing Effective Trust-Aware Recommendation"
- "Toward a Design Space for the Recommendation of Communities"
- "Collaboration and Reputation in Social Web Search"
- "Niche Trend Search for Recommender System based on Knowledgeable Blogger Group"
- "Resource Recommendation for Social Tagging: A Multi-Channel Hybrid Approach"
- "Weighted Content Based Methods for Recommending Connections in Online Social Networks"
HAVE YOU SEEN how the word SIMILARITY has different meanings for different persons or companies?
DO NOT use or DO NOT try to adapt ANY of the FORMULAS suggested by the above papers (for serious online dating proposals) because your Online Dating Site will reach "as low as" 3 to 4 persons high compatible per 1,000 persons screened, so in a 1,000,000 women database, any man will see as many as 3,000 to 4,000 women to contact (nearly at the same time), that means, a whole precision LESS than anyone could achieve by searching on one's own!
In previous posts I had analyzed:
"Personality Similarity calculation paper: A Novel K-Means Based Clustering Algorithm for High Dimensional Data Sets"
and
"Recommender System for Online Dating Service"
If you want to see how to solve that problem, please see the post
"the online dating sound barrier"
The Online Dating Industry for serious daters DOES NOT need to be more social. It needs to be more effective.
The Online Dating Industry for serious daters does not need a 10% improvement, a 50% improvement or a 100% improvement.
It does need "a 100 times better improvement"
Breaking "the online dating sound barrier" is to achieve at least:
3 most compatible persons in a 100,000 persons database.
12 most compatible persons in a 1,000,000 persons database.
48 most compatible persons in a 10,000,000 persons database.
100 times better than Compatibility Matching Algorithms used by actual online dating sites!
The only way to achieve that is:
- using the 16PF5 normative personality test, available in different languages to assess personality of members, or a proprietary test with exactly the same traits of the 16PF5. The ensemble of the 16PF5 is: 10E16, big number as All World Population is nearly 6.7 * 10E9
(WorldWide, there are over 5,000 -five thousand- online dating sites, but no one is using the 16PF5)
- expressing compatibility with eight decimals, like The pattern 6.7.6.8.9.6.7.7.8.7.2.5.8.7.3.4 is 92.55033557% +/- 0.00000001% similar to the pattern 7.7.6.8.8.7.6.5.8.7.4.5.7.7.3.4
Using a quantized pattern comparison method (part of pattern recognition by cross-correlation) to calculate similarity between prospective mates.
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