Wednesday, November 24, 2010

summary of recommender systems

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?
In case you did not notice, recommender systems are morphing to .......... compatibility matching engines!!!
They mostly use the Big5 to assess personality and the Pearson correlation coefficient to calculate similarity.
Researchers in the Personality Based Recommender Systems arena are also testing different / novel formulas to calculate similarity, useless at all because they use the Big5 to assess personality of users.

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.

Psychological-based Recommenders Systems

More about Personality-based Recommender Systems

The NEW era of Personality Based Recommender Systems

Personality Based Recommender Systems

The PLAGUE of recommender systems

Recommender Systems and the Social Web

A Novel K-Means Based Clustering Algorithm for High Dimensional Data Sets

Recommender System for Online Dating Service

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