Tensor-based Collaborative Filtering With Smooth Ratings Scale
원문 초록
Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items while others almost always assign 5 stars to the chosen item. Even if they had experience with the same items this systematic discrepancy in their evaluation style will lead to the systematic errors in the ability of recommender system to effectively extract right patterns from data. To mitigate this problem we introduce the ratings' similarity matrix which represents the dependency between different values of ratings on the population level. Hence, if on average the correlations between ratings exist, it is possible to improve the quality of proposed recommendations by off-setting the effect of either shifted down or shifted up users' rates.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
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
이 논문과 연결된 포럼 글이 아직 없습니다.