Abstract
Users’ behaviors observed in many web-based applications are usually heterogeneous, so modeling
their behaviors considering the interplay among
multiple types of actions is important. However,
recent collaborative filtering (CF) methods based
on a metric learning approach cannot learn multiple types of user actions, because they are developed for only a single type of user actions. This paper proposes a novel metric learning method, called
METAS, to jointly model heterogeneous user behaviors. Specifically, it learns two distinct spaces:
1) action space which captures the relations among
all observed and unobserved actions, and 2) entity
space which captures high-level similarities among
users and among items. Each action vector in the
action space is computed using a non-linear function and its corresponding entity vectors in the entity space. In addition, METAS adopts an efficient
triplet mining algorithm to effectively speed up the
convergence of metric learning. Experimental results show that METAS outperforms the state-ofthe-art methods in predicting users’ heterogeneous
actions, and its entity space represents the useruser and item-item similarities more clearly than
the space trained by the other methods