Improving One-class Recommendation with Multi-tasking on Various Preference Intensities

In general, implicit feedback is easier to obtain than explicit feedback. Thus, making recommendations with only implicit feedback is indispensable. This type of problems are referred to as one-class recommendation [6]. There are several efforts proposed to solve one-class recommendation problems. For example, model-based methods [2, 7] aim to learn a vector representation for each user and item and apply some kernel, such as inner product for matrix factorization (MF) [5], to measure similarity. On the other hand, graph-based methods [11] construct a user-item bipartite graph from historical interactions and utilize random walk on it to explore user interests and make recommendations. In recent years, hybrid approaches [4, 10] combining model-based and graph-based methods have been developed. They explore high-order relationships on the bipartite graph and encode this information into learned entity representations, resulting in remarkable improvements in one-class recommendation tasks. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.