Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Bridging Conversational and Collaborative Signals for Conversational Recommendation
Conversational recommendation systems (CRS) leverage contextual information from conversations to generate recommendations but often struggle due to a lack of collaborative filtering (CF) signals, which capture user-item…
Collaborative FilteringConversational RecommendationRecommendation SystemsEnhancing Recommendation Systems with GNNs and Addressing Over-Smoothing
This paper addresses key challenges in enhancing recommendation systems by leveraging Graph Neural Networks (GNNs) and addressing inherent limitations such as over-smoothing, which reduces model effectiveness as network …
Collaborative FilteringExplainable RecommendationRecommendation SystemsOptimizing LoRa for Edge Computing with TinyML Pipeline for Channel Hopping
We propose to integrate long-distance LongRange (LoRa) communication solution for sending the data from IoT to the edge computing system, by taking advantage of its unlicensed nature and the potential for open source imp…
Collaborative FilteringEdge-computingA Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems
This survey provides an examination of the use of Deep Neural Networks (DNN) in Collaborative Filtering (CF) recommendation systems. As the digital world increasingly relies on data-driven approaches, traditional CF tech…
Collaborative FilteringRecommendation SystemsSurveyConvolutional Transformer Neural Collaborative Filtering
In this study, we introduce Convolutional Transformer Neural Collaborative Filtering (CTNCF), a novel approach aimed at enhancing recommendation systems by effectively capturing high-order structural information in user-…
Collaborative FilteringRecommendation SystemsEnhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders
In the field of Recommender Systems (RS), neural collaborative filtering represents a significant milestone by combining matrix factorization and deep neural networks to achieve promising results. Traditional methods lik…
Collaborative FilteringConformal PredictionDimensionality ReductionPrediction+1How Does Topology Bias Distort Message Passing? A Dirichlet Energy Perspective
Graph-based recommender systems have achieved remarkable effectiveness by modeling high-order interactions between users and items. However, such approaches are significantly undermined by popularity bias, which distorts…
Collaborative FilteringFairnessRecommendation SystemsConsistency Regularization for Complementary Clothing Recommendations
This paper reports on the development of a Consistency Regularized model for Bayesian Personalized Ranking (CR-BPR), addressing to the drawbacks in existing complementary clothing recommendation methods, namely limited c…
Collaborative FilteringProduct RecommendationCoevolution of Opinion Dynamics and Recommendation System: Modeling Analysis and Reinforcement Learning Based Manipulation
In this work, we develop an analytical framework that integrates opinion dynamics with a recommendation system. By incorporating elements such as collaborative filtering, we provide a precise characterization of how reco…
Collaborative FilteringRecommendation SystemsMonolithic Hybrid Recommender System for Suggesting Relevant Movies
Recommendation systems have become the fundamental services to facilitate users information access. Generally, recommendation system works by filtering historical behaviors to understand and learn users preferences. With…
Collaborative FilteringRecommendation SystemsDeBaTeR: Denoising Bipartite Temporal Graph for Recommendation
Due to the difficulty of acquiring large-scale explicit user feedback, implicit feedback (e.g., clicks or other interactions) is widely applied as an alternative source of data, where user-item interactions can be modele…
Collaborative FilteringDenoisingRecommendation SystemsCounterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation
For recommender systems in internet platforms, search activities provide additional insights into user interest through query-click interactions with items, and are thus widely used for enhancing personalized recommendat…
Collaborative FilteringcounterfactualData AugmentationDisentanglement+2Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literature
The indoor environment significantly impacts human health and well-being; enhancing health and reducing energy consumption in these settings is a central research focus. With the advancement of Information and Communicat…
ArticlesCollaborative FilteringProduct RecommendationRecommendation Systems+3Language-Model Prior Overcomes Cold-Start Items
The growth of recommender systems (RecSys) is driven by digitization and the need for personalized content in areas such as e-commerce and video streaming. The content in these systems often changes rapidly and therefore…
Collaborative FilteringLanguage ModelingLanguage Modellingmodel+1Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues
With the rapid growth of digital information, personalized recommendation systems have become an indispensable part of Internet services, especially in the fields of e-commerce, social media, and online entertainment. Ho…
Collaborative FilteringMetric LearningRecommendation SystemsTAGTIMBRE: Efficient Job Recommendation On Heterogeneous Graphs For Professional Recruiters
Job recommendation gathers many challenges well-known in recommender systems. First, it suffers from the cold start problem, with the user (the candidate) and the item (the job) having a very limited lifespan. It makes t…
Collaborative FilteringGraph Neural NetworkRecommendation SystemsDissertation: On the Theoretical Foundation of Model Comparison and Evaluation for Recommender System
Recommender systems have become increasingly important with the rise of the web as a medium for electronic and business transactions. One of the key drivers of this technology is the ease with which users can provide fee…
Collaborative FilteringRecommendation SystemsCo-clustering for Federated Recommender System
As data privacy and security attract increasing attention, Federated Recommender System (FRS) offers a solution that strikes a balance between providing high-quality recommendations and preserving user privacy. However, …
ClusteringCollaborative FilteringContrastive LearningRecommendation SystemsOnline and Offline Evaluations of Collaborative Filtering and Content Based Recommender Systems
Recommender systems are widely used AI applications designed to help users efficiently discover relevant items. The effectiveness of such systems is tied to the satisfaction of both users and providers. However, user sat…
Collaborative FilteringInformation RetrievalRecommendation SystemsGraph Cross-Correlated Network for Recommendation
Collaborative filtering (CF) models have demonstrated remarkable performance in recommender systems, which represent users and items as embedding vectors. Recently, due to the powerful modeling capability of graph neural…
Click-Through Rate PredictionCollaborative FilteringRecommendation Systems