Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Incorporating Recklessness to Collaborative Filtering based Recommender Systems
Recommender systems are intrinsically tied to a reliability/coverage dilemma: The more reliable we desire the forecasts, the more conservative the decision will be and thus, the fewer items will be recommended. This caus…
Collaborative FilteringRecommendation SystemsADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery for reducing patient mortality and enhancing drug safety. Recently, many studies have been devoted to effectively predict…
Collaborative FilteringDrug DiscoveryRepresentation LearningKnowledge-aware Collaborative Filtering with Pre-trained Language Model for Personalized Review-based Rating Prediction
Personalized review-based rating prediction aims at leveraging existing reviews to model user interests and item characteristics for rating prediction. Most of the existing studies mainly encounter two issues. First, the…
Collaborative FilteringLanguage ModelingLanguage ModellingPredictionChallenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
The success of graph neural network-based models (GNNs) has significantly advanced recommender systems by effectively modeling users and items as a bipartite, undirected graph. However, many original graph-based works of…
Collaborative FilteringGraph Neural NetworkRecommendation SystemsAdaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation
Classical recommender systems often assume that historical data are stationary and fail to account for the dynamic nature of user preferences, limiting their ability to provide reliable recommendations in time-sensitive …
Collaborative FilteringProduct RecommendationRecommendation SystemsCollaborative filtering to capture AI user's preferences as norms
Customising AI technologies to each user's preferences is fundamental to them functioning well. Unfortunately, current methods require too much user involvement and fail to capture their true preferences. In fact, to avo…
Collaborative FilteringRecommendation SystemsRecommendation Unlearning via Matrix Correction
Recommender systems are important for providing personalized services to users, but the vast amount of collected user data has raised concerns about privacy (e.g., sensitive data), security (e.g., malicious data) and uti…
Collaborative FilteringRecommendation SystemsDebiased Pairwise Learning from Positive-Unlabeled Implicit Feedback
Learning contrastive representations from pairwise comparisons has achieved remarkable success in various fields, such as natural language processing, computer vision, and information retrieval. Collaborative filtering a…
Collaborative FilteringInformation RetrievalRetrievalLarge Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
Traditional recommender systems leverage users' item preference history to recommend novel content that users may like. However, modern dialog interfaces that allow users to express language-based preferences offer a fun…
Collaborative FilteringRecommendation SystemsSharpness-Aware Graph Collaborative Filtering
Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, GNNs tend to yield inferior performance when the distributions of training and test data are not aligned well. Also, …
Collaborative FilteringLightGT: A Light Graph Transformer for Multimedia Recommendation
Multimedia recommendation methods aim to discover the user preference on the multi-modal information to enhance the collaborative filtering (CF) based recommender system. Nevertheless, they seldom consider the impact of …
Collaborative FilteringMicrovideo RecommendationMicro-video recommendationsMultimedia recommendation+3Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters…
AutoMLCollaborative FilteringGenerative Contrastive Graph Learning for Recommendation
By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering(CF) based recommendation. Recently, researchers have introduced Graph Contrastive Learning(…
Collaborative FilteringContrastive LearningData AugmentationGraph Learning+1Neural Causal Graph Collaborative Filtering
Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF). One classical approach in GCF is to learn u…
Collaborative FilteringGraph LearningGraph Representation LearningRecommendation Systems+2Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search
In e-commerce search, personalized retrieval is a crucial technique for improving user shopping experience. Recent works in this domain have achieved significant improvements by the representation learning paradigm, e.g.…
Collaborative FilteringContrastive LearningGraph LearningRepresentation Learning+1Embedding Mental Health Discourse for Community Recommendation
Our paper investigates the use of discourse embedding techniques to develop a community recommendation system that focuses on mental health support groups on social media. Social media platforms provide a means for users…
Collaborative FilteringTrack Mix Generation on Music Streaming Services using Transformers
This paper introduces Track Mix, a personalized playlist generation system released in 2022 on the music streaming service Deezer. Track Mix automatically generates "mix" playlists inspired by initial music tracks, allow…
Collaborative FilteringGenerative Job Recommendations with Large Language Model
The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process. Predominantly, current job recommendations deploy either collaborative fil…
Collaborative FilteringLanguage ModelingLanguage ModellingLarge Language Model+3Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based models with implicit feedback. In this pape…
Collaborative FilteringA Food Recommender System in Academic Environments Based on Machine Learning Models
Background: People's health depends on the use of proper diet as an important factor. Today, with the increasing mechanization of people's lives, proper eating habits and behaviors are neglected. On the other hand, food …
Collaborative FilteringFeature EngineeringNutritionRecommendation Systems