Enhanced New User Recommendations based on Quantitative Association Rule Mining

In the era of information explosion, how to provide tailored suggestions to a new user is a major concern for collaborative filtering (CF) based recommender systems. The CF recommender system performs very poorly for a new user with very poor profile information. Therefore, we investigate the use of quantitative association rules (QARs) for making recommendations to a new user by exploiting the cold user data which is readily available such as age, gender, occupation, etc. and ratings of items available in the historical data set. The proposed recommendation method, called QARF (QAR based filtering scheme), extracts relationships between readily available information of users and items, and the rating values. Additionally, QARs are extracted during offline processing which optimizes the online computation cost. The discovered rules are then employed during online processing in order to generate recommendations for a new user. Moreover, the QARF recommendation scheme is combined with CF, namely QARF/CF, to further improve recommendation accuracy. Proposed approaches QARF and QARF/CF are evaluated on the platform of MovieLens dataset. Experimental results demonstrate that the proposed schemes enhance new user recommendations and outperform other state of the art CF schemes