An efficient Recommendation System based on the Optimal Stopping Theory

• We propose two mechanisms for a content-based recommender system.
• The proposed mechanisms adopt principles of Optimal Stopping Theory.
• The proposed models maximize the value of the recommendation.
• Our mechanisms do not require any complex modeling.
• Our models do not require any training process.

A Recommendation System (RS) aims to deliver meaningful recommendations to users for items (e.g., music and books), which are of high interest to them. We consider an RS which directly communicates with a set of providers in order to access the information of the items (e.g., descriptions), rate them according to the user’s preferences, and deliver an Item List (IL). The RS is enhanced with a mechanism, which sequentially observes the rating information (e.g., similarity degree) of the items and decides when to deliver the IL to the user, without exhausting the entire set of providers. Hence, the RS saves time and resources. We propose two mechanisms based on the theory of optimal stopping. Both mechanisms deliver an IL, which sufficiently matches to the user’s needs having examined a partial set of items. That is, the number of items in the delivered IL is optimal, producing a high level of user satisfaction, i.e., Quality of Recommendation (QoR). Our simulations reveal the efficiency of the mechanisms and quantify the benefits stemming from their adoption.