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The performance of recommendation systems is highly dependent on candidate matching techniques for scoping users’ information needs. Existing candidate matching methods are based on text embedding and collaborative filtering, which base similarity primarily on semantics or co-occurrences of listings. Unfortunately, they do not leverage valuable user behavior to include recommendation impressions, clicks, or the sequences leading up to a purchase. This rich information reflects accurate user preferences, which have been widely used to enhance the ranking stage of a recommendation system. Yet integrating contextual info into the matching stage is challenging because the feature dimensionality and sparsity will be increased as well. Recently, graph representation learning (GRL) has gained much success in industrial applications like item-to-item recommendation systems. GRL learns a mapping of embedding nodes (with edges) into a low dimensional space by representing users’ behaviours into an activity graph. The goal is to optimize this mapping function so that the learnt geometric relationships can reflect the structural information of the original graph. The trained embeddings can be used as features for downstream applications like nearest neighbor search and ranking problems. Our work focuses on a GRL framework to enhance the performance of the candidate generation. Such an approach inevitably faces the cold start problem. For example, listings with fewer or none user interactions can not be learned effectively. To deal with it, side information like shop, category, price, etc. are integrated into the listing embedding by learning an integrated multi-view embedding.
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