Hypergraph-enhanced multi-interest learning for multi-behavior sequential recommendation

Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. In these platforms, user–item interactive behaviors are often multi-typed (e.g., click, add-to-favorite, purchase) with complex cross-type behavior inter-dependencies. Consequently, the multi-behavior sequence recommendation (MBSR) is gaining growing attention to meet practical application needs. However, most of the existing MBSR methods have not adequately explored the latent multi-dimensional real interests and multi-order multi-behavior dependencies, hampering the accurate inference of user preferences and further limiting recommendation performance. To this end, we devise a Hypergraph-Enhanced Multi-interest Learning Framework (HEML) equipped with a time-sensitive sequence module and a temporal-free hypergraph module, i.e., to learn both multi-interest and multi-behavior dependencies. For multi-interest extraction, a dual-scale transformer is designed to encode sequence patterns from coarse-grained level to fine-grained level, respectively. A classic capsule network is then exploited to extract the hidden two-level multi-interests explicitly. An interest-matching mechanism is presented to further adaptively match the most relevant general interests of the users at the current time. For multi-behavior dependencies, a user-tailored multi-behavior hypergraph is established to capture global multi-order (e.g., triadic or even high-order) dependencies across behaviors. A lightweight hypergraph convolutional network is then designed to perform a two-stage refined ‘node-hyperedge-node’ feature transformation on the hypergraph structure. We also introduce a cross-view co-guided learning mechanism to encourage the aggregation of sequence and hypergraph information across views. Numerous empirical investigations conducted across three authentic datasets demonstrate the consistent superiority of HEML over a diverse array of recommendation methodologies. We have released the implementation code at https://github.com/Breeze-del/HEMLCODE. •Extracting dynamic multiples interests from dual-scale sequential patterns.•Constructing two types of hypergraphs to model global multi-order multi-behavior dependencies.•Fusion of sequential and hypergraph information to learn purchase-oriented preferences.