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Recommender systems have emerged as a new weapon to help online firms torealize many of their strategic goals (e.g., to improve sales, revenue,customer experience etc.). However, many existing techniques commonly approachthese goals by seeking to recover preference (e.g., estimating ratings) in amatrix completion framework. This paper aims to bridge this significant gapbetween the clearly-defined strategic objectives and the not-so-well-justifiedproxy.  We show it is advantageous to think of a recommender system as an analogy toa monopoly economic market with the system as the sole seller, users as thebuyers and items as the goods. This new perspective motivates a game-theoreticformulation for recommendation that enables us to identify the optimalrecommendation policy by explicit optimizing certain strategic goals. In thispaper, we revisit and extend our prior work, the Collaborative-CompetitiveFiltering preference model, towards a game-theoretic framework. The proposedframework consists of two components. First, a conditional preference modelthat characterizes how a user would respond to a recommendation action; Second,knowing in advance how the user would respond, how a recommender system shouldact (i.e., recommend) strategically to maximize its goals. We show howobjectives such as click-through rate, sales revenue and consumption diversitycan be optimized explicitly in this framework. Experiments are conducted on acommercial recommender system and demonstrate promising results.

Collaborative Competitive filtering II: Optimal Recommendation and Collaborative Games