Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Food Pairing Unveiled: Exploring Recipe Creation Dynamics through Recommender Systems
In the early 2000s, renowned chef Heston Blumenthal formulated his "food pairing" hypothesis, positing that if foods share many flavor compounds, then they tend to taste good when eaten together. In 2011, Ahn et al. cond…
Collaborative FilteringRecommendation SystemsLARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation
As online music consumption increasingly shifts towards playlist-based listening, the task of playlist continuation, in which an algorithm suggests songs to extend a playlist in a personalized and musically cohesive mann…
Collaborative FilteringContrastive LearningRepresentation LearningUnifying Graph Convolution and Contrastive Learning in Collaborative Filtering
Graph-based models and contrastive learning have emerged as prominent methods in Collaborative Filtering (CF). While many existing models in CF incorporate these methods in their design, there seems to be a limited depth…
Collaborative FilteringContrastive LearningTransparency, Privacy, and Fairness in Recommender Systems
Recommender systems have become a pervasive part of our daily online experience, and are one of the most widely used applications of artificial intelligence and machine learning. Therefore, regulations and requirements f…
Collaborative FilteringFairnessRecommendation SystemsAn Interpretable Alternative to Neural Representation Learning for Rating Prediction -- Transparent Latent Class Modeling of User Reviews
Nowadays, neural network (NN) and deep learning (DL) techniques are widely adopted in many applications, including recommender systems. Given the sparse and stochastic nature of collaborative filtering (CF) data, recent …
Collaborative FilteringRecommendation SystemsRepresentation LearningPerceptron Collaborative Filtering
While multivariate logistic regression classifiers are a great way of implementing collaborative filtering - a method of making automatic predictions about the interests of a user by collecting preferences or taste infor…
Collaborative FilteringRecommendation SystemsA First Principles Approach to Trust-Based Recommendation Systems in Social Networks
This paper explores recommender systems in social networks which leverage information such as item rating, intra-item similarities, and trust graph. We demonstrate that item-rating information is more influential than ot…
Collaborative FilteringRecommendation SystemsTokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation
There is a growing interest in utilizing large-scale language models (LLMs) to advance next-generation Recommender Systems (RecSys), driven by their outstanding language understanding and in-context learning capabilities…
Collaborative FilteringIn-Context LearningRecommendation SystemsRetrievalHow Powerful is Graph Filtering for Recommendation
It has been shown that the effectiveness of graph convolutional network (GCN) for recommendation is attributed to the spectral graph filtering. Most GCN-based methods consist of a graph filter or followed by a low-rank m…
Collaborative FilteringQAGCF: Graph Collaborative Filtering for Q&A Recommendation
Question and answer (Q&A) platforms usually recommend question-answer pairs to meet users' knowledge acquisition needs, unlike traditional recommendations that recommend only one item. This makes user behaviors more comp…
Collaborative FilteringContrastive LearningGraph Neural NetworkXRec: Large Language Models for Explainable Recommendation
Recommender systems help users navigate information overload by providing personalized recommendations aligned with their preferences. Collaborative Filtering (CF) is a widely adopted approach, but while advanced techniq…
Collaborative FilteringDecision MakingExplainable RecommendationNavigate+2FourierKAN-GCF: Fourier Kolmogorov-Arnold Network -- An Effective and Efficient Feature Transformation for Graph Collaborative Filtering
Graph Collaborative Filtering (GCF) has achieved state-of-the-art performance for recommendation tasks. However, most GCF structures simplify the feature transformation and nonlinear operation during message passing in t…
Collaborative FilteringPopularity-Aware Alignment and Contrast for Mitigating Popularity Bias
Collaborative Filtering (CF) typically suffers from the significant challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to a significant accuracy gap between popul…
Collaborative FilteringContrastive LearningRecommendation SystemsImplicitSLIM and How it Improves Embedding-based Collaborative Filtering
We present ImplicitSLIM, a novel unsupervised learning approach for sparse high-dimensional data, with applications to collaborative filtering. Sparse linear methods (SLIM) and their variations show outstanding performan…
Collaborative FilteringCF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)
Recommender systems are a kind of data filtering that guides the user to interesting and valuable resources within an extensive dataset. by providing suggestions of products that are expected to match their preferences. …
Collaborative FilteringRecommendation SystemsLusifer: LLM-based User SImulated Feedback Environment for online Recommender systems
Reinforcement learning (RL) recommender systems often rely on static datasets that fail to capture the fluid, ever changing nature of user preferences in real-world scenarios. Meanwhile, generative AI techniques have eme…
Collaborative FilteringRecommendation Systemsreinforcement-learningReinforcement Learning+2CELA: Cost-Efficient Language Model Alignment for CTR Prediction
Click-Through Rate (CTR) prediction holds a paramount position in recommender systems. The prevailing ID-based paradigm underperforms in cold-start scenarios due to the skewed distribution of feature frequency. Additiona…
Click-Through Rate PredictionCollaborative FilteringLanguage ModelingLanguage Modelling+1Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering
Intent modeling has attracted widespread attention in recommender systems. As the core motivation behind user selection of items, intent is crucial for elucidating recommendation results. The current mainstream modeling …
Collaborative FilteringGraph GenerationGraph ReconstructionRecommendation SystemsHow to Surprisingly Consider Recommendations? A Knowledge-Graph-based Approach Relying on Complex Network Metrics
Traditional recommendation proposals, including content-based and collaborative filtering, usually focus on similarity between items or users. Existing approaches lack ways of introducing unexpectedness into recommendati…
Collaborative FilteringRecommendation SystemsRerankingDGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting
Multivariate time series forecasting tasks are usually conducted in a channel-dependent (CD) way since it can incorporate more variable-relevant information. However, it may also involve a lot of irrelevant variables, an…
ClusteringCollaborative FilteringGraph ClusteringMultivariate Time Series Forecasting+2