Query-Aware Sequential Recommendation

ABSTRACTSequential recommenders aim to capture users' dynamic interests from their historical action sequences, but remain challenging due to data sparsity issues, as well as the noisy and complex relationships among items in a sequence. Several approaches have sought to alleviate these issues using side-information, such as item content (e.g., images), action types (e.g., click, purchase). While useful, we argue one of the main contextual signals is largely ignored-namely users' queries. When users browse and consume products (e.g., music, movies), their sequential interactions are usually a combination of queries, clicks (etc.). Most interaction datasets discard queries, and corresponding methods simply model sequential behaviors over items and thus ignore this critical context of user interactions. In this work, we argue that user queries should be an important contextual cue for sequential recommendation. First, we propose a new query-aware sequential recommendation setting, i.e. incorpo- rating explicit user queries to model users' intent. Next, we propose a model, namely Query-SeqRec, to (1) incorporate query information into user behavior sequences; and (2) improve model generalization ability using query-item co-occurrence information. Last, we demonstrate the effectiveness of incorporating query features in sequential recommendation on three datasets.1