Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation

TL;DR

This paper introduces a novel sequential recommendation method that models user preference dynamics through dictionary learning and deep autoregressive models, improving prediction accuracy over existing methods.

Contribution

It formulates user preference modeling as a dictionary learning problem combined with a deep autoregressive model, enabling better integration of static and dynamic preferences.

Findings

Captures user preference drifts effectively

Achieves higher accuracy than state-of-the-art methods

Demonstrates effectiveness on multiple datasets

Abstract

Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms -- including both shallow and deep ones -- often model such dynamics independently, i.e., user static and dynamic preferences are not modeled under the same latent space, which makes it difficult to fuse them for recommendation. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences, namely translating sequence to preference. To this end, we formulate the sequential recommendation task as a dictionary learning problem, which learns: 1) a shared dictionary matrix, each row of which represents a partial signal of user dynamic preferences shared across users; and 2) a posterior distribution estimator using a deep autoregressive model integrated with Gated Recurrent Unit (GRU), which can select related rows of the dictionary to represent a user's dynamic preferences conditioned on his/her past behaviors. Qualitative studies on the Netflix dataset demonstrate that the proposed method can capture the user preference drifts over time and quantitative studies on multiple real-world datasets demonstrate that the proposed method can achieve higher accuracy compared with state-of-the-art factorization and neural sequential recommendation methods. The code is available at https://github.com/cchao0116/S2PNM-TKDE2021.