  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 a multi-interest
extractor that embeds the multiple user interests based on the user
interactions, and a multi-interest aggregator that aggregates the learned
multi-interest embeddings to derive the final 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 multi-interest extractor and aggregator, while ignoring their
different learning objectives, and 2) they merely consider the centrality of
the user interactions to embed multiple interests of the user, 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, making them adaptive to the extractor and
aggregator. Moreover, we utilize both the mean and variance embeddings of user
interactions to embed the user multiple interests for the comprehensively user
interest learning. We conduct extensive experiments on three public datasets,
and the results verify that our proposed PoMRec outperforms the
state-of-the-art multi-interest learning methods.
