Personalizing Similar Product Recommendations in Fashion E-commerce

In fashion e-commerce platforms, product discovery is one of the key components of a good user experience. There are numerous ways using which people find the products they desire. Similar product recommendations is one of the popular modes using which users find products that resonate with their intent. Generally these recommendations are not personalized to a specific user. Traditionally, collaborative filtering based approaches have been popular in the literature for recommending non-personalized products given a query product. Also, there has been focus on personalizing the product listing for a given user. In this paper, we marry these approaches so that users will be recommended with personalized similar products. Our experimental results on a large fashion e-commerce platform (Myntra) show that we can improve the key metrics by applying personalization on similar product recommendations.

[1]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[2]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[3]  Xing Xie,et al.  xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems , 2018, KDD.

[4]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[5]  Julian J. McAuley,et al.  VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback , 2015, AAAI.

[6]  Jay P. Trivedi,et al.  Investigating the Factors That Make a Fashion App Successful: The Moderating Role of Personalization , 2018 .

[7]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[8]  Chen Fang,et al.  Visually-Aware Fashion Recommendation and Design with Generative Image Models , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[9]  Amber Madvariya,et al.  Deciphering Fashion Sensibility Using Community Detection , 2017 .

[10]  Paul Covington,et al.  Deep Neural Networks for YouTube Recommendations , 2016, RecSys.

[11]  Lars Schmidt-Thieme,et al.  Fast context-aware recommendations with factorization machines , 2011, SIGIR.

[12]  Gediminas Adomavicius,et al.  Context-aware recommender systems , 2008, RecSys '08.

[13]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[14]  Benjamin Schrauwen,et al.  Deep content-based music recommendation , 2013, NIPS.

[15]  Amber Madvariya,et al.  Discovering Similar Products in Fashion E-commerce , 2017, eCOM@SIGIR.

[16]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[17]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.