Multiple Interest and Fine Granularity Network for User Modeling

• URL: http://arxiv.org/abs/2112.02591v1
• Date: Sun, 5 Dec 2021 15:12:08 GMT
• Title: Multiple Interest and Fine Granularity Network for User Modeling
• Authors: Jiaxuan Xie, Jianxiong Wei, Qingsong Hua, Yu Zhang
• Abstract summary: User modeling plays a fundamental role in industrial recommender systems, either in the matching stage and the ranking stage, in terms of both the customer experience and business revenue. Most existing deep-learning based approaches exploit item-ids and category-ids but neglect fine-grained features like color and mate-rial, which hinders modeling the fine granularity of users' interests. We present Multiple interest and Fine granularity Net-work (MFN), which tackle users' multiple and fine-grained interests and construct the model from both the similarity relationship and the combination relationship among the users' multiple interests.
• Score: 3.508126539399186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: User modeling plays a fundamental role in industrial recommender systems, either in the matching stage and the ranking stage, in terms of both the customer experience and business revenue. How to extract users' multiple interests effectively from their historical behavior sequences to improve the relevance and personalization of the recommend results remains an open problem for user modeling.Most existing deep-learning based approaches exploit item-ids and category-ids but neglect fine-grained features like color and mate-rial, which hinders modeling the fine granularity of users' interests.In the paper, we present Multiple interest and Fine granularity Net-work (MFN), which tackle users' multiple and fine-grained interests and construct the model from both the similarity relationship and the combination relationship among the users' multiple interests.Specifically, for modeling the similarity relationship, we leverage two sets of embeddings, where one is the fixed embedding from pre-trained models (e.g. Glove) to give the attention weights and the other is trainable embedding to be trained with MFN together.For modeling the combination relationship, self-attentive layers are exploited to build the higher order combinations of different interest representations. In the construction of network, we design an interest-extract module using attention mechanism to capture multiple interest representations from user historical behavior sequences and leverage an auxiliary loss to boost the distinction of the interest representations. Then a hierarchical network is applied to model the attention relation between the multiple interest vectors of different granularities and the target item. We evaluate MFNon both public and industrial datasets. The experimental results demonstrate that the proposed MFN achieves superior performance than other existed representing methods.

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