  Sequential recommender systems (SRS) could capture dynamic user preferences
by modeling historical behaviors ordered in time. Despite effectiveness,
focusing only on the \textit{collaborative signals} from behaviors does not
fully grasp user interests. It is also significant to model the
\textit{semantic relatedness} reflected in content features, e.g., images and
text. Towards that end, in this paper, we aim to enhance the SRS tasks by
effectively unifying collaborative signals and semantic relatedness together.
Notably, we empirically point out that it is nontrivial to achieve such a goal
due to semantic gap issues. Thus, we propose an end-to-end two-stream
architecture for sequential recommendation, named TSSR, to learn user
preferences from ID-based and content-based sequence. Specifically, we first
present novel hierarchical contrasting module, including coarse user-grained
and fine item-grained terms, to align the representations of inter-modality.
Furthermore, we also design a two-stream architecture to learn the dependence
of intra-modality sequence and the complex interactions of inter-modality
sequence, which can yield more expressive capacity in understanding user
interests. We conduct extensive experiments on five public datasets. The
experimental results show that the TSSR could yield superior performance than
competitive baselines. We also make our experimental codes publicly available
at https://anonymous.4open.science/r/TSSR-2A27/.
