Recommender systems for social networks analysis and mining: precision versus diversity

Recommender systems has become increasingly important in online community for providing personalized services and products to users. Traditionally, performance of recommender algorithms has been evaluated based on accuracy and the focus of the research was on providing accurate recommendation lists. However, recently diversity and novelty of recommendation lists have been introduced as key issues in designing recommender systems. In general, novelty/diversity and accuracy do not go hand in hand. Therefore, designing models answering novelty/diversity-accuracy dilemma is one of the challenging problems in the context of practical recommender systems. In this paper, we first introduce the diversity-accuracy challenge in recommender systems, and then present two recommendation algorithms which approach the problem from two perspectives. The first model is a filtering algorithm to select candidate items which incorporates timing information of ratings to improve both accuracy and novelty of recommender systems. The filter can be applied as adds-on to any recommender algorithm. The second model is a probabilistic model which resolves the dilemma and provides adjustable level of accuracy and diversity that can be tuned by a single parameter