Improving product discovery via Hierarchical Recommendations!
A recommendation engine’s primary goal is to surface personalised & relevant content to the user, content which satisfies explicit intent as well as serendipitous content that would otherwise be invisible. E-commerce categories such as Lifestyle, have a lot of flux, the trends last for a short time window and have their demand distributed across an extensive selection. In such cases, recommending product collections can be a better idea instead of individual products.
In this talk I will be talking about the recommendation system at Flipkart, our journey towards recommending collection and how it improved the product discovery and helped in solving cold start problem. I will also cover the relevance algorithm used which is a hybrid of collaborative and content-based recommendation, and how this is achieved at scale, where we have catalogue consisting of ~400M products and this will be ever growing in this ecommerce world.
The Why ?
1. To solve cold start problem and to improve product discovery which aids to coverage
2. Increasing basket size and improving engagement post purchase
3. Cross category discovery and search result augmentation
- Introduction - a generic e-commerce Recommender System
- Problem in a traditional Recommender System
- Our journey towards recommending collections
- Flipkart’s Taxonomy
- Computing relevance via Cosine Similarity
- Multiple approaches
- Improvement and results
Neha is a Senior Software Engineer with Recommendation team at Flipkart. She has worked on building scalable systems for product recommendations and personalisation. In the past she has also worked on Natural Language Processing. She is interested in building robust data processing pipelines at scale, and applying Machine Learning to solve challenging problems . She has graduated from IIT BHU. While not working on official projects, she involves herself in technical writing and blogging. She also contributes to the open source world by answering technical questions.