FineRec:Exploring Fine-grained Sequential Recommendation
arxiv(2024)
摘要
Sequential recommendation is dedicated to offering items of interest for
users based on their history behaviors. The attribute-opinion pairs, expressed
by users in their reviews for items, provide the potentials to capture user
preferences and item characteristics at a fine-grained level. To this end, we
propose a novel framework FineRec that explores the attribute-opinion pairs of
reviews to finely handle sequential recommendation. Specifically, we utilize a
large language model to extract attribute-opinion pairs from reviews. For each
attribute, a unique attribute-specific user-opinion-item graph is created,
where corresponding opinions serve as the edges linking heterogeneous user and
item nodes. To tackle the diversity of opinions, we devise a diversity-aware
convolution operation to aggregate information within the graphs, enabling
attribute-specific user and item representation learning. Ultimately, we
present an interaction-driven fusion mechanism to integrate attribute-specific
user/item representations across all attributes for generating recommendations.
Extensive experiments conducted on several realworld datasets demonstrate the
superiority of our FineRec over existing state-of-the-art methods. Further
analysis also verifies the effectiveness of our fine-grained manner in handling
the task.
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