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A context-aware recommendation approach based on feature selection

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Abstract

Contextual information can be used in recommender systems to make recommendation more efficient. Recent research has made progress in combining contextual information into representation models for recommendations. However, the existing approaches do not well address the problem of data sparsity, and they suffer from context redundancy. To deal with these problems, this paper proposes a context-aware recommendation approach based on embedded feature selection. It gets rid of context redundancy by generating a minimum subset of all contextual information and allocates the weight to each context appropriately. Experiments on the restaurant recommendation shows that the proposed approach has better performance.

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Acknowledgments

This paper was supported by the National Social Science Foundation of China (No. 18ZDA086) and the National Natural Science Foundation of China (Nos. 71501010, 71661167009).

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  1. Beijing Jiaotong Univeristy, Beijing, China

    Lei Chen & Meimei Xia

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  1. Lei Chen
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  2. Meimei Xia
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Correspondence to Meimei Xia.

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Chen, L., Xia, M. A context-aware recommendation approach based on feature selection. Appl Intell 51, 865–875 (2021). https://doi.org/10.1007/s10489-020-01835-9

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