Learning to Hash for Recommendation with Tensor Data

Recommender systems usually need to compare user interests and item characteristics in the context of large user and item space, making hashing based algorithms a promising strategy to speed up recommendation. Existing hashing based recommendation methods only model the users and items and dealing with the matrix data, e.g., user-item rating matrix. In practice, recommendation scenarios can be rather complex, e.g., collaborative retrieval and personalized tag recommendation. The above scenarios generally need fast search for one type of entities (target entities) using multiple types of entities (source entities). The resulting three or higher order tensor data makes conventional hashing algorithms fail for the above scenarios. In this paper, a novel hashing method is accordingly proposed to solve the above problem, where the tensor data is approached by properly designing the similarities between the source entities and target entities in Hamming space. Besides, operator matrices are further developed to explore the relationship between different types of source entities, resulting in auxiliary codes for source entities. Extensive experiments on two tasks, i.e., personalized tag recommendation and collaborative retrieval, demonstrate that the proposed method performs well for tensor data.