Mixed factorization for collaborative recommendation with heterogeneous explicit feedbacks

Collaborative recommendation (CR) is a fundamental enabling technology for providing high-quality personalization services in various online and offline applications. Collaborative recommendation with heterogeneous explicit feedbacks (CR-HEF) such as 5-star grade scores and like/dislike binary ratings is a new and important problem, because it provides a rich and accurate source for learning users’ preferences. However, most previous works on collaborative recommendation only focus on exploiting homogeneous explicit feedbacks such as grade scores or homogeneous implicit feedbacks such as clicks or purchases. In this paper, we study the CR-HEF problem, and design a novel and generic mixed factorization based transfer learning framework to fully exploit those two different types of explicit feedbacks. Experimental results on two CR-HEF tasks with real-world data sets show that our TMF is able to perform significantly better than the state-of-the-art methods.