Abstract
This paper presents an approach for building a recommender system that makes use of heterogeneous side information based on a modification of Deep Structured Semantic Model (DSSM). The core idea is to unite all side-information features into two subnetworks of user-related and item-related features and learn the similarity between their latent representations using neural matrix factorization. We tested the proposed model in the task of products recommendation on the dataset provided by a Russian online-marketing company. To deal with the sparsity of the data, we suggest recommending categories of items first and then use any other algorithm to rank items inside categories. We compared the performance of the proposed model to several traditional methods and demonstrated that DSSM with heterogeneous input significantly increases overall recommendation quality which makes it suitable for recommendations with rich side information about items and users.
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Notes
- 1.
In fact, we also tested a novel collaborative model named CoFFee [12].
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Acknowledgments
We are very grateful to Anton Lozhkov and Diana Khakimova for the efforts in revising the present paper in the very last minutes before the deadline.
This research was supported by the Russian Research Foundation grant no. 19-11-00281.
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Larionova, A., Kazakova, P., Nikitinsky, N. (2019). Deep Structured Semantic Model for Recommendations in E-commerce. In: Pérez GarcÃa, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado RodrÃguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_8
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