Abstract
Related product recommendation (RPR) is pivotal to the success of any e-commerce service. In this paper, we deal with the problem of recommending related products i.e., given a query product, we would like to suggest top-k products that have high likelihood to be bought together with it. Our problem implicitly assumes asymmetry i.e., for a phone, we would like to recommend a suitable phone case, but for a phone case, it may not be apt to recommend a phone because customers typically would purchase a phone case only while owning a phone. We also do not limit ourselves to complementary or substitute product recommendation. For example, for a specific night wear t-shirt, we can suggest similar t-shirts as well as track pants. So, the notion of relatedness is subjective to the query product and dependent on customer preferences. Further, various factors such as product price, availability lead to presence of selection bias in the historical purchase data, that needs to be controlled for while training related product recommendations model. These challenges are orthogonal to each other deeming our problem non-trivial. To address these, we propose DAEMON, a novel Graph Neural Network (GNN) based framework for related product recommendation, wherein the problem is formulated as a node recommendation task on a directed product graph. In order to capture product asymmetry, we employ an asymmetric loss function and learn dual embeddings for each product, by appropriately aggregating features from its neighborhood. DAEMON leverages multi-modal data sources such as catalog metadata, browse behavioral logs to mitigate selection bias and generate recommendations for cold-start products. Extensive offline experiments show that DAEMON outperforms state-of-the-art baselines by 30–160% in terms of HitRate and MRR for the node recommendation task. In the case of link prediction task, DAEMON presents 4–16% AUC gains over state-of-the-art baselines. DAEMON delivers significant improvement in revenue and sales as measured through an A/B experiment.
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Notes
- 1.
We use product, item and node interchangeably in this paper.
- 2.
We use product and node interchangeably in this paper based on the context.
- 3.
Results are relative to the co-purchase baseline and absolute numbers are not presented due to confidentiality.
- 4.
Results are relative to the co-purchase baseline and absolute numbers are not presented due to confidentiality.
- 5.
Results are relative to the R-GCN baseline and absolute numbers are not presented due to confidentiality.
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Virinchi, S., Saladi, A., Mondal, A. (2023). Recommending Related Products Using Graph Neural Networks in Directed Graphs. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13713. Springer, Cham. https://doi.org/10.1007/978-3-031-26387-3_33
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