With the explosive growth of online social networks, it is now well understood that social information is highly helpful to recommender systems. Social recommendation methods are capable of battling the critical cold-start issue, and thus can greatly improve prediction accuracy. The main intuition is that through trust and influence, users are more likely to develop affinity toward items consumed by their social ties. Despite considerable work in social recommendation, little attention has been paid to the important distinctions between strong and weak ties, two well-documented notions in social sciences. In this work, we study the effects of distinguishing strong and weak ties in social recommendation. We use neighbourhood overlap to approximate tie strength and extend the popular Bayesian Personalized Ranking (BPR) model to incorporate the distinction of strong and weak ties. We present an EM-based algorithm that simultaneously classifies strong and weak ties in a social network w.r.t. optimal recommendation accuracy and learns latent feature vectors for all users and all items. We conduct extensive empirical evaluation on four real-world datasets and demonstrate that our proposed method significantly outperforms state-of-the-art pairwise ranking methods in a variety of accuracy metrics.
PAPER Identifying Opportunities for Valuable Encounters: Toward Context-Aware Social Matching Systems
PAPER: Similarity Metrics from Social Network Analysis for Content Recommender Systems
PAPER: INNOVATIVE PERSONALITY-BASED DIGITAL SERVICES
PAPER: Romantic Partnerships and the Dispersion of Social Ties
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)
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.
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.
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