{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/search-behavior-prediction-a-hypergraph","title":"Search Behavior Prediction: A Hypergraph Perspective","arxiv_id":"2211.13328","date":"2022-11-23","proceeding":null,"authors":["Yan Han","Edward W Huang","Wenqing Zheng","Nikhil Rao","Zhangyang Wang","Karthik Subbian"],"abstract":"Although the bipartite shopping graphs are straightforward to model search behavior, they suffer from two challenges: 1) The majority of items are sporadically searched and hence have noisy/sparse query associations, leading to a \\textit{long-tail} distribution. 2) Infrequent queries are more likely to link to popular items, leading to another hurdle known as \\textit{disassortative mixing}. To address these two challenges, we go beyond the bipartite graph to take a hypergraph perspective, introducing a new paradigm that leverages \\underline{auxiliary} information from anonymized customer engagement sessions to assist the \\underline{main task} of query-item link prediction. This auxiliary information is available at web scale in the form of search logs. We treat all items appearing in the same customer session as a single hyperedge. The hypothesis is that items in a customer session are unified by a common shopping interest. With these hyperedges, we augment the original bipartite graph into a new \\textit{hypergraph}. We develop a \\textit{\\textbf{D}ual-\\textbf{C}hannel \\textbf{A}ttention-Based \\textbf{H}ypergraph Neural Network} (\\textbf{DCAH}), which synergizes information from two potentially noisy sources (original query-item edges and item-item hyperedges). In this way, items on the tail are better connected due to the extra hyperedges, thereby enhancing their link prediction performance. We further integrate DCAH with self-supervised graph pre-training and/or DropEdge training, both of which effectively alleviate disassortative mixing. Extensive experiments on three proprietary E-Commerce datasets show that DCAH yields significant improvements of up to \\textbf{24.6\\% in mean reciprocal rank (MRR)} and \\textbf{48.3\\% in recall} compared to GNN-based baselines. Our source code is available at \\url{https://github.com/amazon-science/dual-channel-hypergraph-neural-network}.","url_abs":"https://arxiv.org/abs/2211.13328v2","url_pdf":"https://arxiv.org/pdf/2211.13328v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"search-behavior-prediction-a-hypergraph","repo_url":"https://github.com/amazon-science/dual-channel-hypergraph-neural-network","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2211.13328","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}