Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks
Implicit feedbacks have recently received much attention in recommendation communities due to their close relationship with real industry problem settings. However, most works only exploit users homogeneous implicit feedbacks such as users transaction records from bought activities, and ignore the other type of implicit feedbacks like examination records from browsed activities. The latter are usually more abundant though they are associated with high uncertainty w.r.t. users true preferences. In this paper, we study a new recommendation problem called heterogeneous implicit feedbacks (HIF), where the fundamental challenge is the uncertainty of the examination records. As a response, we design a novel preference learning algorithm to learn a confidence for each uncertain examination record with the help of transaction records. Specifically, we generalize Bayesian personalized ranking (BPR), a seminal pairwise learning algorithm for homogeneous implicit feedbacks, and learn the confidence adaptively, which is thus called adaptive Bayesian personalized ranking (ABPR). ABPR has the merits of uncertainty reduction on examination records and accurate pairwise preference learning on implicit feedbacks. Experimental results on two public data sets show that ABPR is able to leverage uncertain examination records effectively, and can achieve better recommendation performance than the state-of-the-art algorithm on various ranking-oriented evaluation metrics.
adaptive bayesian personalized ranking heterogeneous implicit feedbacks preference learning collaborative filtering transfer learning