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Register a new userLarge Scale Product Graph Construction for Recommendation in E-commerce
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Added by
Xiaoyong Yang
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Building a recommendation system that serves billions of users on daily basis is a challenging problem, as the system needs to make astronomical number of predictions per second based on real-time user behaviors with O(1) time complexity. Such kind of large scale recommendation systems usually rely heavily on pre-built index of products to speedup the recommendation service so that online user waiting time is un-noticeable. One important indexing structure is the product-product index, where one can retrieval a list of ranked products given a seed product. The index can be viewed as a weighted product-product graph. In this paper, we present our novel technologies to efficiently build such kind of indexed product graphs. In particular, we propose the Swing algorithm to capture the substitute relationships between products, which can utilize the substructures of user-item click bi-partitive graph. Then we propose the Surprise algorithm for the modeling of complementary product relationships, which utilizes product category information and solves the sparsity problem of user co-purchasing graph via clustering technique. Base on these two approaches, we can build the basis product graph for recommendation in Taobao. The approaches are evaluated comprehensively with both offline and online experiments, and the results demonstrate the effectiveness and efficiency of the work.
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Recently, a new form of online shopping becomes more and more popular, which combines live streaming with E-Commerce activity. The streamers introduce products and interact with their audiences, and hence greatly improve the performance of selling products. Despite of the successful applications in industries, the live stream E-commerce has not been well studied in the data science community. To fill this gap, we investigate this brand-new scenario and collect a real-world Live Stream E-Commerce (LSEC) dataset. Different from conventional E-commerce activities, the streamers play a pivotal role in the LSEC events. Hence, the key is to make full use of rich interaction information among streamers, users, and products. We first conduct data analysis on the tripartite interaction data and quantify the streamers influence on users purchase behavior. Based on the analysis results, we model the tripartite information as a heterogeneous graph, which can be decomposed to multiple bipartite graphs in order to better capture the influence. We propose a novel Live Stream E-Commerce Graph Neural Network framework (LSEC-GNN) to learn the node representations of each bipartite graph, and further design a multi-task learning approach to improve product recommendation. Extensive experiments on two real-world datasets with different scales show that our method can significantly outperform various baseline approaches.
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