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Personalizing Similar Product Recommendations in Fashion E-commerce

1 Pith paper cite this work. Polarity classification is still indexing.

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abstract

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.

fields

cs.LG 1

years

2019 1

verdicts

UNVERDICTED 1

representative citing papers

One Embedding To Do Them All

cs.LG · 2019-06-28 · unverdicted · novelty 5.0

Unified embeddings trained on text, clickstream, and image data perform uniformly well on three unrelated e-commerce tasks.

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  • One Embedding To Do Them All cs.LG · 2019-06-28 · unverdicted · none · ref 26 · internal anchor

    Unified embeddings trained on text, clickstream, and image data perform uniformly well on three unrelated e-commerce tasks.