RecSys 2011 Conference

Recommender systems (a.k.a recommendation engines) can be based on:
- past actions (as the formely Beacon at Facebook)
- a pattern of personal preferences (by collaborative filtering, as the actual one at Facebook) The main disadvantage with recommendation engines based on collaborative filtering is when users instead of providing their personal preference try to guess the global preference and they introduce bias in the recommendation algorithm.
- personality traits of users.
Personality Based Recommender Systems are the next generation of recommender systems because they perform FAR better than Behavioural ones (past actions and pattern of personal preferences)
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?

RecSys 2011

Accepted Papers


Featured paper:
"Stochastic Matching and Collaborative Filtering to Recommend People to People"
useless at all for online dating purposes because:
The Online Dating Industry needs innovations but they will come from only one source: the latest discoveries in theories of romantic relationships development with commitment.
I) Several studies showing contraceptive pills users make different mate choices, on average, compared to non-users.
II) People often report partner preferences that are not compatible with their choices in real life.
III) 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.

Please remember ALL personality based recommender systems for online dating purposes ARE ... compatibility matching algorithms!!!
That is nothing new, nothing innovative. Online Dating Sites like eHarmony, Parship, Be2, MeeticAffinity and others had been calculating personality similarity between prospective users since several years ago with low successful rates, with a low effectiveness/efficiency level of their matching algorithms (less than 10%) because they use the normative Big5 or ipsative proprietary models instead -like Chemistry or PerfectMatch- to measure personality traits.

No one is using the 16PF5 to assess personality of members.
No one calculates similarity with a quantized pattern comparison method.
No one can show Compatibility Distribution Curves to each and every of its members.

There are other similar papers (People Recommenders) useless at all for the Online Dating Industry like:
"CCR - A Content-Collaborative Reciprocal Recommender for Online Dating"
"Explicit and implicit user preferences in online dating. "
"Finding someone you will like and who won't reject you."
"Learning User Preferences in Online Dating."