A multistep priority-based ranking for top-N recommendation using social and tag information

Recommender system is a collection of information retrieval tools and techniques used for recommending items to users based on their choices. For improving recommendation accuracy, the use of extra information (e.g., social, trust, item tags, etc.) other than user-item rating data remains an active area of research since last one and half decade. In this paper, we propose a novel methodology for top-N item recommendation, which uses three different kinds of information: user-item rating data, social network among the users, and tags associated with the items. The proposed method has mainly five steps: (i) creation of neighbor users’ item set, (ii) construction of the user-feature matrix, (iii) computation of user priority, (iv) computation of item priority, and finally, (v) recommendation based on the item priority. We implement the proposed methodology with three recommendation dataset. We compare our results with that of the obtained from some state-of-the-art ranking methods and observe that recommendation accuracy is improved in the case of the proposed algorithm for both all users and cold-start users scenarios. The algorithm is also able to generate more cold-start items in the recommended item list.