Tuesday, October 25, 2016

PAPER Personalized Recommender System based on Friendship Strength in Social Network Services


The rapid growth of social network services has produced a considerable amount of data, called big social data. Big social data are helpful for improving personalized recommender systems because these enormous data have various characteristics. Therefore, many personalized recommender systems based on big social data have been proposed, in particular models that use people relationship information. However, most existing studies have provided recommendations on special purpose and single-domain SNS that have a set of users with similar tastes, such as MovieLens and Last.fm; nonetheless, they have considered closeness relation. In this paper, we introduce an appropriate measure to calculate the closeness between users in a social circle, namely, the friendship strength. Further, we propose a friendship strength-based personalized recommender system that recommends topics or interests users might have in order to analyze big social data, using Twitter in particular. The proposed measure provides precise recommendations in multi-domain environments that have various topics. We evaluated the proposed system using one month's Twitter data based on various evaluation metrics. Our experimental results show that our personalized recommender system outperforms the baseline systems, and friendship strength is of great importance in personalized recommendation.

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.

In case you had not noticed, recommender systems are morphing to compatibility matching engines, as the same used in the Online Dating Industry. 

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 test or similar to assess personality traits and a new method to calculate similarity between quantized patterns.

Online Dating sites have very big databases, in the range of 20,000,000 (twenty million) profiles, so the Big Five model or the HEXACO model are not enough for predictive purposes. That is why I suggest the 16PF5 test instead and another method to calculate similarity.

High precision in matching algorithms is precisely the key to open the door and leave the infancy of compatibility testing.
Without offering the NORMATIVE 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.

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"

All other proposals are NOISE and perform as placebo.

Please see also:

Very easy to copycat eHarmony, but very difficult to innovate: a 100 times better algorithm than eHarmony.

PAPER Improved Scalable Recommender System (2016)

Proceedings of the 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE) 2016


No comments:

Post a Comment