Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation

摘要

Collaborative recommendation has attracted various research works in recent years. However, an important problem setting, i.e., “a user examined several items but only rated a few”, has not received much attention yet. We coin this problem heterogeneous collaborative recommendation (HCR) from the perspective of users’ heterogeneous feedbacks of implicit examinations and explicit ratings. In order to fully exploit such different types of feedbacks, we propose a novel and generic solution called compressed knowledge transfer via factorization machine (CKT-FM). Specifically, we assume that the compressed knowledge of user homophily and item correlation, i.e., user groups and item sets behind two types of feedbacks, are similar and then design a two-step transfer learning solution including compressed knowledge mining and integration. Our solution is able to transfer high quality knowledge via noise reduction, to model rich pairwise interactions among individual-level and cluster-level entities, and to adapt the potential inconsistent knowledge from implicit feedbacks to explicit feedbacks. Furthermore, the analysis on time complexity and space complexity shows that our solution is much more efficient than the state-of-the-art method for heterogeneous feedbacks. Extensive empirical studies on two large data sets show that our solution is significantly better than the state-of-the-art non-transfer learning method w.r.t. recommendation accuracy, and is much more efficient than that of leveraging the raw implicit examinations directly instead of compressed knowledge w.r.t. CPU time and memory usage. Hence, our CKT-FM strikes a good balance between effectiveness and efficiency of knowledge transfer in HCR.