BAR: Behavior-aware recommendation for sequential heterogeneous one-class collaborative filtering

In our daily life, we are often greatly assisted with recommendation engines in finding the required information efficiently and accurately. In this paper, we focus on an emerging and important recommendation problem in enormous real-world applications, i.e., sequential heterogeneous one-class collaborative filtering (SHOCCF). In the studied problem, we have some users' sequential and heterogeneous one-class feedback, i.e., sequences of (item, behavior) pairs, where the behaviors can be of different types such as examinations and purchases. We propose a generic solution called behavior-aware recommendation (BAR), which is able to adapt an existing RNN-, CNN-, attention- or GNN-based sequential recommendation method to SHOCCF. The main idea of our BAR is to provide the behavior information to the input and output of a representation module that models the item sequence. Specifically, we design a behavior attention layer that uses the behavior at the next timestamp in order to digest the behaviors at different positions in the sequence and obtain more accurate attention scores that will be fed to the input of the representation module. Moreover, we further design a task-specific layer to fuse the real behavior at the next timestamp with the sequential feature generated by the representation module to distinguish different prediction tasks w.r.t. the behavior types. We then conduct extensive empirical studies on four public datasets and find that our BAR is able to significantly improve the performance of a certain sequential recommendation method when it is adapted to SHOCCF. (c) 2022 Elsevier Inc. All rights reserved.