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Capturing user intent from a short interaction sequence imposes a variety of challenges since no user profiles are available and interaction data is naturally sparse. Recent approaches relying on graph neural networks (GNNs) for session-based recommendation use global item relations to explore collaborative information from different sessions. These methods capture the topological structure of the graph and rely on multi-hop information aggregation in GNNs to exchange information along edges. Consequently, graph-based models suffer from noisy item relations in the training data and introduce high complexity for large item catalogs. We propose to explicitly model the multi-hop information aggregation mechanism over multiple layers via shortest-path edges based on knowledge from the sequential recommendation domain. Our approach does not require multiple layers to exchange information and ignores unreliable item-item relations. Furthermore, to address inherent data sparsity, we are the first to apply supervised contrastive learning by mining data-driven positive and hard negative item samples from the training data. Extensive experiments on four different datasets show that the proposed approach outperforms almost all of the state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3701764","type":"journal-article","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T10:11:48Z","timestamp":1730196708000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Efficient Session-based Recommendation with Contrastive Graph-based Shortest Path Search"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7337-524X","authenticated-orcid":false,"given":"Andreas","family":"Peintner","sequence":"first","affiliation":[{"name":"University of Innsbruck, Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3934-6941","authenticated-orcid":false,"given":"Amir Reza","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"University of Innsbruck, Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3195-8273","authenticated-orcid":false,"given":"Eva","family":"Zangerle","sequence":"additional","affiliation":[{"name":"University of Innsbruck, Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,10]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"Proceedings of the 20th International Conference on Very Large Data Bases (VLDB'94)","unstructured":"Rakesh Agrawal and Ramakrishnan Srikant. 1994. 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