A novel combination recommendation method for solving sparse and cold start problems

Considering the problems resulting from the traditional recommended approaches which are powerless to address the well-known cold-start and data sparseness, and the fact that most currently existing association rule mining(ARM) algorithms were designed with basket-oriented analysis in mind, which are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user, this paper introduces a novel association recommendation method based on combination similarity, and proposes a solution to the cold start problem by combining association rules and collaborative filtering techniques. The proposed method focuses on mining rules for only one target user or target item at a time, while utilizing the interest factor to balance the weight between active users (or items) and non active users (or items), which in order to recommend an optimal solution (rules) via weighted method. To recommend both high ratings and collection of items with high similarity, the similarity measurement method was used to filter low similarity items, and to provide the final results by combining the association rules and CF recommendation, realizing user-based or item-based collaborative filtering recommendation. Experiments on the MovieLens data set reveals that the results obtained from employing this method has significantly better than the publishecl results and that it is better able to deal with sparse data and cold start problems.