  Modern online service providers such as online shopping platforms often
provide both search and recommendation (S&R) services to meet different user
needs. Rarely has there been any effective means of incorporating user behavior
data from both S&R services. Most existing approaches either simply treat S&R
behaviors separately, or jointly optimize them by aggregating data from both
services, ignoring the fact that user intents in S&R can be distinctively
different. In our paper, we propose a Search-Enhanced framework for the
Sequential Recommendation (SESRec) that leverages users' search interests for
recommendation, by disentangling similar and dissimilar representations within
S&R behaviors. Specifically, SESRec first aligns query and item embeddings
based on users' query-item interactions for the computations of their
similarities. Two transformer encoders are used to learn the contextual
representations of S&R behaviors independently. Then a contrastive learning
task is designed to supervise the disentanglement of similar and dissimilar
representations from behavior sequences of S&R. Finally, we extract user
interests by the attention mechanism from three perspectives, i.e., the
contextual representations, the two separated behaviors containing similar and
dissimilar interests. Extensive experiments on both industrial and public
datasets demonstrate that SESRec consistently outperforms state-of-the-art
models. Empirical studies further validate that SESRec successfully disentangle
similar and dissimilar user interests from their S&R behaviors.
