arXiv:2305.07516
Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis
원문 초록
Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking data to inform a collaborative filtering model for movie recommendation providing an improvement over the click-based implementations and additionally analyze the area of interest (AOI) duration as related to the known information of click data and movies seen previously, showing AOI information consistently coincides with these items of interest.
함께 살펴볼 논문
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NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering
Uncertainty in Repeated Implicit Feedback as a Measure of Reliability
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
토론
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