Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Multi-Objective Personalized Product Retrieval in Taobao Search
In large-scale e-commerce platforms like Taobao, it is a big challenge to retrieve products that satisfy users from billions of candidates. This has been a common concern of academia and industry. Recently, plenty of wor…
Collaborative FilteringRetrievalScientific and Technological News Recommendation Based on Knowledge Graph with User Perception
Existing research usually utilizes side information such as social network or item attributes to improve the performance of collaborative filtering-based recommender systems. In this paper, the knowledge graph with user …
Collaborative FilteringNews RecommendationRecommendation SystemsRelationRestricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering
Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial intelligence. Therefore, those models that are able to provide not only predi…
ClassificationCollaborative FilteringPredictionRecommendation SystemsThe Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing m…
Collaborative FilteringDiversityMetric LearningRecommendation SystemsEfficient Graph based Recommender System with Weighted Averaging of Messages
We showcase a novel solution to a recommendation system problem where we face a perpetual soft item cold start issue. Our system aims to recommend demanded products to prospective sellers for listing in Amazon stores. Th…
Collaborative FilteringGraph AttentionRecommendation SystemsSmart Meters Integration in Distribution System State Estimation with Collaborative Filtering and Deep Gaussian Process
The problem of state estimations for electric distribution system is considered. A collaborative filtering approach is proposed in this paper to integrate the slow time-scale smart meter measurements in the distribution …
Collaborative FilteringState EstimationDiscussion about Attacks and Defenses for Fair and Robust Recommendation System Design
Information has exploded on the Internet and mobile with the advent of the big data era. In particular, recommendation systems are widely used to help consumers who struggle to select the best products among such a large…
Collaborative FilteringFairnessPositionRecommendation SystemsSpatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
Session-based recommendation (SBR) systems aim to utilize the user's short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating…
Collaborative FilteringContrastive LearningSession-Based RecommendationsThe effectiveness of factorization and similarity blending
Collaborative Filtering (CF) is a widely used technique which allows to leverage past users' preferences data to identify behavioural patterns and exploit them to predict custom recommendations. In this work, we illustra…
Collaborative FilteringEfficient Beam Search for Initial Access Using Collaborative Filtering
Beamforming-capable antenna arrays overcome the high free-space path loss at higher carrier frequencies. However, the beams must be properly aligned to ensure that the highest power is radiated towards (and received by) …
Collaborative FilteringRecommendation SystemsA challenge-based survey of e-recruitment recommendation systems
E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of the job seekers for the positions as well as the job seekers…
Collaborative FilteringRecommendation SystemsSurveySUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation
Collaborative filtering problems are commonly solved based on matrix completion techniques which recover the missing values of user-item interaction matrices. In a matrix, the rating position specifically represents the …
Collaborative FilteringMatrix CompletionMissing ValuesPosition+3Fair Matrix Factorisation for Large-Scale Recommender Systems
Recommender systems are hedged with various requirements, such as ranking quality, optimisation efficiency, and item fairness. Item fairness is an emerging yet impending issue in practical systems. The notion of item fai…
Collaborative FilteringFairnessRecommendation SystemsTag-Aware Document Representation for Research Paper Recommendation
Finding online research papers relevant to one's interests is very challenging due to the increasing number of publications. Therefore, personalized research paper recommendation has become a significant and timely resea…
Collaborative FilteringTAGOnline Low Rank Matrix Completion
We study the problem of {\em online} low-rank matrix completion with $\mathsf{M}$ users, $\mathsf{N}$ items and $\mathsf{T}$ rounds. In each round, the algorithm recommends one item per user, for which it gets a (noisy) …
ClusteringCollaborative FilteringLow-Rank Matrix CompletionMatrix CompletionUser recommendation system based on MIND dataset
Nowadays, it's a very significant way for researchers and other individuals to achieve their interests because it provides short solutions to satisfy their demands. Because there are so many pieces of information on the …
Collaborative FilteringNews RecommendationRecommendation SystemsWord EmbeddingsA Scalable Recommendation Engine for New Users and Items
In many digital contexts such as online news and e-tailing with many new users and items, recommendation systems face several challenges: i) how to make initial recommendations to users with little or no response history…
Collaborative FilteringRecommendation SystemsHidden Author Bias in Book Recommendation
Collaborative filtering algorithms have the advantage of not requiring sensitive user or item information to provide recommendations. However, they still suffer from fairness related issues, like popularity bias. In this…
Collaborative FilteringFairnessExtracting Relations Between Sectors
The term "sector" in professional business life is a vague concept since companies tend to identify themselves as operating in multiple sectors simultaneously. This ambiguity poses problems in recommending jobs to job se…
Collaborative FilteringCausal Inference in Recommender Systems: A Survey and Future Directions
Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering…
Causal InferenceClick-Through Rate PredictionCollaborative FilteringRecommendation Systems+1