Deep Structured Semantic Model for Recommendations in E-commerce

• Anna Larionova ^[2] ; Polina Kazakova ^[3] ; Nikita Nikitinsky ^[1]
  1. [1] National University of Science and Technology

     National University of Science and Technology

     Rusia

     
  2. [2] Picturer LLC, Petrovka Street 20 Building 1, (127051 Moscow, Russia)
  3. [3] Integrated Systems, Vorontsovskaya Street 35B Building 3, Room 413 (109147 Moscow, Russia)

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• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 85-96
• Idioma: inglés
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  • 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.