Collaborative Ranking Tags and Items via Cross-domain Recommendation

Tag and item recommendation are both important tasks in recommender systems. Item recommendation, learning user-item rating patterns, helps users to discover a list of items that are more likely to match their interests. On the other hand, tag recommendation, exploiting relationships of user-tag-item, assists users to find a personalized list of tags that can precisely describe their experience on items. Existing methods often consider these two tasks independently. However, these two tasks are inherently related because they both use information from same groups of users and items. In other words, it is the same users who give ratings and annotate tags to the same set of items. Tags may explain why a user purchase an item. For instance, customers on Yelp not only give a Live star rating for a restaurant but also leave comments/tags to express their positive sentiments. Users with similar interests might give similar ratings and post similar tags to certain items. The co-occurrence of users, items and tags motivates us to jointly perform item and tag recommendation to better model users' behavior by leveraging their personalized items and tags. In this work, we propose a cross-domain learning method that seamlessly integrates tag and item recommendation into one unified framework. In particular, we utilize the knowledge transferred between two tasks to collaboratively rank items and tags for users through coupled tensor-matrix factorization. We also provide a principled way to incorporate additional auxiliary information about users (e.g., users' relationships in a social network) and items (e.g., descriptions of items in Wikipedia) to improve recommendation accuracy for both tasks. Empirical results demonstrate the effectiveness of our proposed model.