Papers Collaborative Filtering
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Fair Augmentation for Graph Collaborative Filtering
Recent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users' preferences from user-item networks. Despite emerging regulations addressing fairness of aut…
Collaborative FilteringFairnessCoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation
Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by concatenating collaborative features with text tokens in…
Collaborative FilteringGeneral KnowledgeWorld KnowledgeEasyRec: Simple yet Effective Language Models for Recommendation
Deep neural networks have become a powerful technique for learning representations from user-item interaction data in collaborative filtering (CF) for recommender systems. However, many existing methods heavily rely on u…
Collaborative FilteringContrastive LearningLanguage ModellingRecommendation Systems+1Collaborative Cross-modal Fusion with Large Language Model for Recommendation
Despite the success of conventional collaborative filtering (CF) approaches for recommendation systems, they exhibit limitations in leveraging semantic knowledge within the textual attributes of users and items. Recent f…
Collaborative FilteringLanguage ModelingLanguage ModellingLarge Language Model+1Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy in a federated setting. Existing FedCF methods typically combine distributed Col…
Collaborative FilteringDecoderPrivacy PreservingValue predictionGraphTransfer: A Generic Feature Fusion Framework for Collaborative Filtering
Graph Neural Networks (GNNs) have demonstrated effectiveness in collaborative filtering tasks due to their ability to extract powerful structural features. However, combining the graph features extracted from user-item i…
Collaborative FilteringFeedback Reciprocal Graph Collaborative Filtering
Collaborative filtering on user-item interaction graphs has achieved success in the industrial recommendation. However, recommending users' truly fascinated items poses a seesaw dilemma for collaborative filtering models…
Collaborative FilteringContrastive LearningRecommendation SystemsSimple but Efficient: A Multi-Scenario Nearline Retrieval Framework for Recommendation on Taobao
In recommendation systems, the matching stage is becoming increasingly critical, serving as the upper limit for the entire recommendation process. Recently, some studies have started to explore the use of multi-scenario …
Collaborative FilteringRecommendation SystemsRetrievalModality-Balanced Learning for Multimedia Recommendation
Many recommender models have been proposed to investigate how to incorporate multimodal content information into traditional collaborative filtering framework effectively. The use of multimodal information is expected to…
Collaborative FilteringcounterfactualCounterfactual InferenceKnowledge Distillation+2Text-Driven Neural Collaborative Filtering Model for Paper Source Tracing
Identifying significant references within the complex interrelations of a citation knowledge graph is challenging, which encompasses connections through citations, authorship, keywords, and other relational attributes. T…
ArticlesCollaborative FilteringLanguage ModelingLanguage ModellingL^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering
Graph neural networks (GNNs) have recently emerged as an effective approach to model neighborhood signals in collaborative filtering. Towards this research line, graph contrastive learning (GCL) demonstrates robust capab…
Collaborative FilteringContrastive LearningData AugmentationA Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice
Recommender Systems (RS) play an integral role in enhancing user experiences by providing personalized item suggestions. This survey reviews the progress in RS inclusively from 2017 to 2024, effectively connecting theore…
Collaborative FilteringFairnessRecommendation SystemsSurveySocialRec: User Activity Based Post Weighted Dynamic Personalized Post Recommendation System in Social Media
User activities can influence their subsequent interactions with a post, generating interest in the user. Typically, users interact with posts from friends by commenting and using reaction emojis, reflecting their level …
Collaborative FilteringAdvancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation
Using 286 research papers collected from Web of Science, ScienceDirect, SpringerLink, arXiv, and Google Scholar databases, a systematic review methodology was adopted to review and summarize the current challenges and po…
Causal InferenceCollaborative FilteringData PoisoningDeep Reinforcement Learning+3Language Representations Can be What Recommenders Need: Findings and Potentials
Recent studies empirically indicate that language models (LMs) encode rich world knowledge beyond mere semantics, attracting significant attention across various fields. However, in the recommendation domain, it remains …
Collaborative FilteringContrastive LearningLanguage ModelingLanguage Modelling+2ITEM: Improving Training and Evaluation of Message-Passing based GNNs for top-k recommendation
Graph Neural Networks (GNNs), especially message-passing-based models, have become prominent in top-k recommendation tasks, outperforming matrix factorization models due to their ability to efficiently aggregate informat…
Collaborative FilteringAmplify Graph Learning for Recommendation via Sparsity Completion
Graph learning models have been widely deployed in collaborative filtering (CF) based recommendation systems. Due to the issue of data sparsity, the graph structure of the original input lacks potential positive preferen…
Collaborative FilteringGraph LearningGraph Neural NetworkMatrix Completion+2A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems
Since the creation of the Web, recommender systems (RSs) have been an indispensable mechanism in information filtering. State-of-the-art RSs primarily depend on categorical features, which ecoded by embedding vectors, re…
BenchmarkingCollaborative FilteringModel SelectionRaspberry Pi 4+1SimCE: Simplifying Cross-Entropy Loss for Collaborative Filtering
The learning objective is integral to collaborative filtering systems, where the Bayesian Personalized Ranking (BPR) loss is widely used for learning informative backbones. However, BPR often experiences slow convergence…
Collaborative FilteringRecommendation SystemsEvaluating Ensemble Methods for News Recommender Systems
News recommendation is crucial for facilitating individuals' access to articles, particularly amid the increasingly digital landscape of news consumption. Consequently, extensive research is dedicated to News Recommender…
ArticlesCollaborative FilteringNews RecommendationRecommendation Systems