Complementary-Product Recommendation Algorithm

References

Tong Zhao. (2020). “Improving complementary-product recommendations." Blog post
- QUOTE: ... That improvement comes from three main strategies: better selection of training data for the CPR model; greater diversity in the types of products recommended; and respect for the asymmetry of the CPR problem (while an SD card may a be a good product to complement a camera, a camera is not a good product to complement an SD card).
  Our approach also addresses the problem of cold start, or predicting complementary products for items that were added to the product catalogue after the machine learning model was trained. To do that, we use an embedding scheme developed at Amazon, called Product2vec, to represent the inputs to the CPR model — the products we seek to complement — according to their attributes and their relationships with other products, rather than simply using their names or ID numbers. …

(Yu, Litchfield et al., 2019) ⇒ Hang Yu, Lester Litchfield, Thomas Kernreiter, Seamus Jolly, and Kathryn Hempstalk. (2019). “Complementary Recommendations: A Brief Survey.” In: The Proceedings of the 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). DOI:10.1109/HPBDIS.2019.8735479.
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https://tech.ebayinc.com/engineering/complementary-item-recommendations-at-ebay-scale/
- QUOTE: ... Generating relevant complementary item recommendations that drive conversion at eBay is no easy task. eBay is an e-commerce marketplace with 1.2 billion items and 179 million buyers, where users can buy and sell virtually anything. In addition to the challenge of the large scale, there is limited structured data attributes, such as ISBN, available for these items, which makes it difficult to use traditional collaborative filtering approaches for generating recommendations.
  The inventory is also volatile; some items on eBay are listed for just a week and never appear again. Given all of these constraints, it is difficult to even generate similar item recommendations given an input seed item (example: seed = iPhone 7 32GB, recommendation = iPhone 7 64GB). Generating items that would complement the seed item, so that the seed and recommended items might be purchased together in a bundle for example, is even more challenging (example: seed = iPhone 7 32GB, recommendation = iPhone 7 case). Here, we describe the complementary items algorithm we developed to solve this task. …

(Kang et al., 2019) ⇒ Wang-Cheng Kang, Eric Kim, Jure Leskovec, Charles Rosenberg, and Julian McAuley. (2019). “Complete the Look: Scene-based Complementary Product Recommendation.” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10532-10541.

(McAuley et al., 2015a) ⇒ Julian McAuley, Rahul Pandey, and Jure Leskovec. (2015). “Inferring Networks of Substitutable and Complementary Products.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783381
- QUOTE: ... To design a useful recommender system, it is important to understand how products relate to each other. For example, while a user is browsing mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. In economics, these two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Such relationships are essential as they help us to identify items that are relevant to a user's search. …