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Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.)widely exist in most real-world recommendation scenarios, which are beneficialto learn users' multi-faceted preferences. As dependencies are explicitlyexhibited by the multiple types of behaviors, effectively modeling complexbehavior dependencies is crucial for multi-behavior prediction. Thestate-of-the-art multi-behavior models learn behavior dependenciesindistinguishably with all historical interactions as input. However, differentbehaviors may reflect different aspects of user preference, which means thatsome irrelevant interactions may play as noises to the target behavior to bepredicted. To address the aforementioned limitations, we introducemulti-interest learning to the multi-behavior recommendation. Morespecifically, we propose a novel Coarse-to-fine Knowledge-enhancedMulti-interest Learning (CKML) framework to learn shared and behavior-specificinterests for different behaviors. CKML introduces two advanced modules, namelyCoarse-grained Interest Extracting (CIE) and Fine-grained BehavioralCorrelation (FBC), which work jointly to capture fine-grained behavioraldependencies. CIE uses knowledge-aware information to extract initialrepresentations of each interest. FBC incorporates a dynamic routing scheme tofurther assign each behavior among interests. Additionally, we use theself-attention mechanism to correlate different behavioral information at theinterest level. Empirical results on three real-world datasets verify theeffectiveness and efficiency of our model in exploiting multi-behavior data.Further experiments demonstrate the effectiveness of each module and therobustness and superiority of the shared and specific modelling paradigm formulti-behavior data.

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation