Sunday, December 15, 2013

PAPER: Personality-Based Active Learning

Personality-Based Active Learning for Collaborative Filtering Recommender Systems

Abstract. Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items.
Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the Recommender system.
In this paper, we propose a novel AL approach that exploits
the user's personality - using the Five Factor Model (FFM) - in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, context-aware RS that provides users with recommendations for places of interest (POIs).
We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.

Please notice:
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. They need to calculate personality similarity between users but there are different formulas to calculate similarity. In case you had not noticed, recommender systems are morphing to .......... compatibility matching engines, as the same used in the Online Dating Industry since years, with low success rates until now because they mostly use the Big5 to assess personality and the Pearson correlation coefficient to calculate similarity. 
The BIG 5 (Big Five) normative personality test is OBSOLETE. Do not use it any more. The HEXACO (a.k.a. Big Six) is another oversimplification. Online Dating sites have very big databases, in the range of 20,000,000 (twenty million) profiles, so the BIG5 model or the HEXACO model are not enough for predictive purposes. That is why I suggest the 16PF5 test instead.

Which is the right approach to innovate in the Personality Based Recommender Systems Arena?
The same approach to innovate in the Online Dating Industry == 16PF5 or 15FQ+ test or similar to assess personality traits and a new method to calculate similarity between quantized patterns.

What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test.
The market remains enormous!!

Please remember: "similarity is actually the key to a long-lasting relationship "

- Innovations for the Online Dating Industry.
C level executives are cooking barbecues under the water (selling smoke) and not paying attention to latest research from Academics which could be beneficial for the Online Dating Industry. Wasting precious time with Matchmakers which should be obsolete.  

eHarmony , True, PerfectMatch, MeeticAffinity, Parship, Be2, PlentyOfFish, Chemistry and others have a low effectiveness/efficiency level of their matching algorithms.
The Online Dating Industry does not need a 10% improvement, a 50% improvement or a 100% improvement. It does need "a 100 times better improvement" , a BIG innovation.

Actual matching algorithms used by eHarmony, Chemistry, PlentyOfFish and others, even behavioural recommender systems, can not be improved, they need to be discarded NOW. Because they are in the range of 3 to 4 prospective mates as selected / recommended / compatible for dating purposes per 1,000 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 firing flowers.
They can not break the online dating sound barrier!
You do not need to improve a piston engine when you need a jet engine to break sound barrier.

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