ECRT: Flexible Sequence Enhancement Framework for Cross-Domain Information Reuse in Recommendation

Chronological sequence of user-item interactions is a key feature in recommender systems, as it reveals the transition of users' interests as well as contextual relevance between nearby items. In modern e-commerce applications, various scenarios are usually integrated in one entry page, and the behavior sequence tend to be a combination of user-item interactions across multiple domains, such as on-sale goods, search queries, short videos, livestreams, etc. However, traditional domain-specified recommendations only deal with the interactions within the target domain, which neglects the overall profiles depicted by the behavior across the entire application, leading to overestimation of retargeted items as well as underestimation of unseen ones. So it is crucial to leverage cross-domain data from prominent domains to better supplement user behavior sequences for our targets. To tackle this problem, we propose the Enhanced Cross-domain Ralation Transfer (ECRT) framework to make flexible sequence augmentation with the assist of cross-domain information from other domains. We first employ similarity-based retrieval to obtain relevant sequence information from neighbor domains and build a heterogeneous graph to represent the complex behavior of users. Then we use innovative mining approaches to sample relevant information from the graph to supplement users' behavior sequences, and a hierarchical gated attention structure is used to aggregate these augmented information. We apply our proposed method in the livestream recommendation of Taobao channel pages, and the final experimental results indicate that our method demonstrates excellent performance in both online and offline environments, with an excess of up to 3.6% in main online indicators beyond past SOTA methods.