Collaborative filtering with sequential implicit feedback via learning users' preferences over item-sets.

Nowadays, the sequential information, i.e., the ordering of the recorded feedback, is one of the most frequently used auxiliary information for developing recommendation algorithms. However, there are still some challenges in collaborative filtering with sequential implicit feedback. For one thing, there may be some co-occurrence and "leap-occurrence" phenomena in users' behavior sequences. For another, a weak point of utilizing implicit feedback is that the unobserved behaviors do not always indicate "dislike". In this paper, we focus on these two fundamental challenges in users' preference learning, i.e., the uncertainty of the ordering of the next items and the uneven quality of the negative samples, and propose a novel solution named COFIS (short for co llaborative fi ltering with s equential implicit feedback via learning users' preferences over item-sets). Specifically, our COFIS incorporates two learning paradigms (i.e., pairwise and pointwise) and two expression strategies (i.e., "MOO" and "MOS", representing two different ways to treat the negative item-sets when compared with the positive item-sets). Our COFIS can be applied to existing methods such as many factorization- and deep learning-baseed models. We derive four specific algorithms of our COFIS based on Fossil (denoted as COFIS(-,-) for short) in particular, and further show the results of our COFIS-improved methods (including the factorization-based COFIS, and the deep learning-based SASRec(COFIS) and FMLP-Rec(COFIS)). Notice that we treat the seminal works and their pointwise version as special cases of our COFIS. Empirical studies on four real-world datasets show the superiority of our proposed COFIS compared with the state-of-the-art baselines. Moreover, we also study the impact of some key parameters in our COFIS. [ABSTRACT FROM AUTHOR]