Item-based recommendation with Shapley value

Discovering knowledge in archival data is the goal of researchers. One of them is collaborative filtering recommender system is developing fastly today. It may be rather effective in sparse and "long tail" datasets. Calculating to make decision based on many criteria is really necessary. Relationships, interactions between criteria need to have been fully considered, decision will be more reliable and feasible. In this paper, we propose a new approach that builds a recommender decision-making model based on importance of item, set of items with Shapley value. This model also incorporates traditional techniques and some our new approaches and was tested, evaluated on multirecsys tool we develope from some available tools and uses standardized datasets to experiment. Experimental results show that the proposed model is always satisfactory and reliable. They can be applied in appropriate contexts to minimize limitations of recommender system today and is a research way next time.