Year: 2023
Type: conference-paper
Abstract: Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task. Existing group recommendation methods usually infer groups’ preferences via aggregating diverse members’ interests. Actually, groups’ ultimate choice involves compromises between members, and finally, an agreement can be reached. However, existing individual information aggregation lacks a holistic group-level consideration, failing to capture the consensus information. Besides, their specific aggregation strategies either suffer from high computational costs or become too coarse-grained to make precise predictions.
Source: ACM Web Conference (WWW)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: G1200806771, III: Small: Exploiting the Massive User Generated Utterances for Intent Mining under Scarce Annotations, III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation, III: Medium: Collaborative Research: Self-Supervised Recommender System Learning with Application Specific Adaption, III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation, III: Medium: Collaborative Research: Self-Supervised Recommender System Learning with Application Specific Adaption, G4815838450, G5340839507, G544703200, G6898366254, SaTC: CORE: Small: Collaborative: Learning Dynamic and Robust Defenses Against Co-Adaptive Spammers
OpenAlex ID: W4319653986