Collaborative filtering recommendations based on multi-factor random walks

Using trust relationships can improve the accuracy of recommendation systems; however, it is affected by data sparsity. Random walks can harvest the behavioral relationships between users and compensate for data sparsity. However, existing random walk methods may generate information of little value or even interference, which affects recommendation accuracy. A collaborative filter recommendation based on multi-factor random walk was proposed to address these problems. In this method, the comprehensive trust values of the current user over other users based on the rating time, user attribute preference, and number of mutual friends were computed more accurately to determine the trust neighborhood of the current user. Thus, trustworthy users with preferences similar to those of the current user were accurately and conveniently obtained to act as neighboring users, and the sparsity of the trust relationship could be alleviated. The final predicted rating of the target item was obtained using the ratings of multiple neighboring users for the target item or similar items to improve recommendation accuracy. Moreover, to avoid generating ratings that affect the prediction accuracy during the walk, a decision to stop the walk was made based on the comprehensive trust value, item similarity, and depth of the current walk, further improving the recommendation accuracy. An evaluation was performed on two datasets, and the proposed method achieved superior prediction accuracy and coverage rate even with sparse data and exhibited a high recommendation accuracy even with cold-start users.