{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/e04154b0-f713-4f35-9eb1-798965f36c25","identifier":"e04154b0-f713-4f35-9eb1-798965f36c25","url":"https://froggit.ai/public/capsules/e04154b0-f713-4f35-9eb1-798965f36c25","name":"ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation","text":"# ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation\n\nSource-backed public reference for arXiv:2604.14114.\n\n**Authors:** Xiaofan Zhou, Kyumin Lee\n**Primary source:** https://arxiv.org/abs/2604.14114\n**Published:** 2026-04-15T17:36:19Z\n**Updated:** 2026-04-15T17:36:19Z\n**Categories:** cs.IR, cs.LG\n\n## Abstract Summary\nSequential recommendation has become increasingly prominent in both academia and industry, particularly in e-commerce. The primary goal is to extract user preferences from historical interaction sequences and predict items a user is likely to engage with next. Recent advances have leveraged contrastive learning and graph neural networks to learn more expressive representations from interaction histories -- graphs capture relational structure between nodes, while ID-based representations encode item-specific information. However, few studies have explored multi-view contrastive learning between ID and graph perspectives to jointly improve user and item representations, especially in settings where only interaction data is available without auxiliary information. To address this gap, we propose Multi-View Contrastive learning for sequential recommendation (MVCrec), a framework that integrates complementary signals from both sequential (ID-based) and graph-based views. MVCrec incorporates three contrastive objectives: within the sequential view, within the graph view, and across views. To effectively fuse the learned representations, we introduce a multi-view attention fusion module that combines global and local attention mechanisms to estimate the likelihood of a target user...\n\n## Public Use Notes\n- This capsule summarizes the paper's arXiv metadata and abstract; it is not an independent replication or endorsement of the paper's claims.\n- Use it as a cited research reference for discovery, retrieval, and agent context.\n- For clinical, security, operational, or deployment-sensitive topics, treat the pape","keywords":["cs.IR","cs.LG"],"about":[],"citation":["https://arxiv.org/abs/2604.14114"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-04-16T06:00:04.141000Z","dateModified":"2026-06-19T14:20:13Z","isBasedOn":"https://arxiv.org/abs/2604.14114","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":40},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"},{"@type":"PropertyValue","name":"content_hash","value":"d66378d63cd3a59349d491caafb8349c9257db602b6e3bf40bb948e963e79790"}]}