Optimal Recommendations Under Attraction, Aversion, and Social Influence

ACM 2014 International Conference on Knowledge Discovery and Data Mining (SIGKDD 2014)


People's interests are dynamically evolving, often affected by external factors such as trends promoted by the media or adopted by their friends. In this work, we model interest evolution through dynamic interest cascades: we consider a scenario where a user's interests may be affected by (a) the interests of other users in her social circle, as well as (b) suggestions she receives from a recommender system. In the latter case, we model user reactions through either attraction or aversion towards past suggestions.

We study this interest evolution process, and the utility accrued by recommendations, as a function of the system's recommendation strategy. We show that, in steady state, the optimal strategy can be computed as the solution of a semi-definite program (SDP). Using datasets of user ratings, we provide evidence for the existence of aversion and attraction in real-life data, and show that our optimal strategy can lead to significantly improved recommendations over systems that ignore aversion and attraction.

Modeling User Interest Evolution: A Graphical Illustration



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  author = {Wei Lu and Stratis Ioannidis and Smriti Bhagat and Laks V.S. Lakshmanan},
  title = {Optimal Recommendations under Attraction, Aversion, and Social Influence},
  booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year = {2014},
  pages = {811-820},
  url = {http://dx.doi.org/10.1145/2623330.2623744},
  doi = {10.1145/2623330.2623744},
  publisher = {ACM},
  address = {New York, NY, USA}