Complementary-Product Recommendation Task

Context:
- It can be solved by a Complementary-Product Recommendation System (that implements a complementary-product recommendation algorithm).
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Example(s):
- a Complementary Videogame Add-On Product Recommendation Task, for video game add-ons.
- anchor product = Tennis Racket, recommendation = tennis balls.
- anchor product = Digital Camera, recommendation = SD cards.
- anchor product = iPhone 12 32GB, recommendation = iPhone 12 case.
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Counter-Example(s):
- a Substituting-Product Recommendation Task.
See: Accessory Product.

References

Tong Zhao. (2020). “Improving complementary-product recommendations." Blog post
- QUOTE: ... One way that e-commerce sites make life easier for customers is by recommending products that complement whatever the customer is looking for: someone buying a tennis racket, for instance, may also want to buy tennis balls; someone buying a camera may want an SD card for extra storage. …

(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.
- QUOTE: ... For recommender systems, the ultimate target is to stimulate the purchasing activities of potential customers by retrieving the items that catch their personalized interests among the overloaded information. With this goal, such systems can be classified as substitute and complementary recommenders: substitute recommenders offer similar items to the source, complementary recommenders suggest items that are dissimilar to the source but are often sold with it as a companion item or service (such as a mobile phone (source) and case (complementary) ). …

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. …