Prompt-based Multi-interest Learning Method for Sequential Recommendation
CoRR(2024)
摘要
Multi-interest learning method for sequential recommendation aims to predict
the next item according to user multi-faceted interests given the user
historical interactions. Existing methods mainly consist of two modules: the
multi-interest extraction module that learns user multi-interest embeddings to
capture the user multi-interests, and the multi-interest weight prediction
module that learns the weight of each interest for aggregating the learned
multi-interest embeddings to derive the user embedding, used for predicting the
user rating to an item. Despite their effectiveness, existing methods have two
key limitations: 1) they directly feed the user interactions into the two
modules, while ignoring their different learning objectives, and 2) they merely
consider the centrality of the user interactions to learn the user
multi-interests, while overlooking their dispersion. To tackle these
limitations, we propose a prompt-based multi-interest learning method (PoMRec),
where specific prompts are inserted into user interactions to make them
adaptive to different learning objectives of the two modules. Moreover, we
utilize both the mean and variance embeddings of user interactions to derive
the user multi-interest embeddings for comprehensively model the user
multi-interests. We conduct extensive experiments on two public datasets, and
the results verify that our proposed PoMRec outperforms the state-of-the-art
multi-interest learning methods.
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