  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.
