Saloni Agarwal
ABSTRACT
In e-commerce, a group of similar or complementary products is recommended as a bundle based on the product category. Existing work on modeling bundle recommendations consists of graph-based approaches. In these methods, user-product interactions provide a more personalized experience. Moreover, these approaches require robust user-product interactions and cannot be applied to cold start scenarios. When a new product is launched or for products with limited purchase history, the lack of user-product interactions will render these algorithms inaccessible. Hence, no bundles recommendations will be provided to users for such product categories. These scenarios are frequent for retailers like Target, where much of the stock is seasonal, and new brands are launched throughout the year. This work alleviates this problem by modeling product bundles recommendation as a supervised graph link prediction problem. A graph neural network (GNN) based product bundles recommendation system, BundlesSEAL is presented. First, we build a graph using add-to-cart data and then use BundlesSEAL to predict the link representing bundles relation between products represented as nodes. We also propose a heuristic to identify relevant pairs of products for efficient inference. Further, we also apply BundlesSEAL for predicting the edge weights instead of just link existence. BundlesSEAL based link prediction leads to amelioration of the above-mentioned cold start problem by increasing the coverage of product bundles recommendations in various categories by 50% while achieving a 35% increase in revenue over behavioral baseline. The model was also validated over the Amazon product metadata dataset.
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Index Terms
- Improving Bundles Recommendation Coverage in Sparse Product Graphs
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