Papers Collaborative Filtering
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RecBaselines2023: a new dataset for choosing baselines for recommender models
The number of proposed recommender algorithms continues to grow. The authors propose new approaches and compare them with existing models, called baselines. Due to the large number of recommender models, it is difficult …
Collaborative FilteringDescriptiveCross-domain Recommender Systems via Multimodal Domain Adaptation
Collaborative Filtering (CF) has emerged as one of the most prominent implementation strategies for building recommender systems. The key idea is to exploit the usage patterns of individuals to generate personalized reco…
Collaborative FilteringDomain AdaptationEntity AlignmentRecommendation Systems+1Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization
Collaborative filtering (CF) has become a popular method for developing recommender systems (RSs) where ratings of a user for new items are predicted based on her past preferences and available preference information of …
Collaborative FilteringData AugmentationRecommendation SystemsMB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation
Collaborative filtering-based recommender systems that rely on a single type of behavior often encounter serious sparsity issues in real-world applications, leading to unsatisfactory performance. Multi-behavior Recommend…
Collaborative FilteringMulti-Task LearningRecommendation SystemsMean-Variance Efficient Collaborative Filtering for Stock Recommendation
The rise of FinTech has transformed financial services onto online platforms, yet stock investment recommender systems have received limited attention compared to other industries. Personalized stock recommendations can …
Collaborative FilteringComputational EfficiencyRecommendation SystemsSafe Collaborative Filtering
Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples with…
Collaborative FilteringComputational EfficiencyDecision MakingFairness+1On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
Collaborative filtering (CF) is an important research direction in recommender systems that aims to make recommendations given the information on user-item interactions. Graph CF has attracted more and more attention in …
Collaborative FilteringRecommendation SystemsGraph Transformer for Recommendation
This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture. We highlight the importance of high-quality d…
Collaborative FilteringData AugmentationRecommendation SystemsRepresentation Learning+1Representer Point Selection for Explaining Regularized High-dimensional Models
We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model in terms of importance weights for each o…
Binary ClassificationCollaborative FilteringRecommendation SystemsThe News Delivery Channel Recommendation Based on Granular Neural Network
With the continuous maturation and expansion of neural network technology, deep neural networks have been widely utilized as the fundamental building blocks of deep learning in a variety of applications, including speech…
Collaborative FilteringDeep LearningMachine TranslationNews Recommendation+3Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation
The use of graph convolution in the development of recommender system algorithms has recently achieved state-of-the-art results in the collaborative filtering task (CF). While it has been demonstrated that the graph conv…
Collaborative FilteringRecommendation SystemsRepresentation LearningGraph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding
Conversational AI systems such as Alexa need to understand defective queries to ensure robust conversational understanding and reduce user friction. These defective queries often arise from user ambiguities, mistakes, or…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Collaborative FilteringFriction+8uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering
Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield recommendation lists with popularity bias. Previous studies have utilized inverse propens…
Causal InferenceCollaborative FilteringRepresentation LearningExploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights
Text-based collaborative filtering (TCF) has become the mainstream approach for text and news recommendation, utilizing text encoders, also known as language models (LMs), to represent items. However, existing TCF models…
Collaborative FilteringNews RecommendationRecommendation SystemsPreference or Intent? Double Disentangled Collaborative Filtering
People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both intent and preference factors are usuall…
Collaborative FilteringDisentanglementIntent RecognitionRepresentation LearningAdaptive Graph Contrastive Learning for Recommendation
Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering (CF) approaches for recommender systems. The key idea of GNN-based recommender systems is to recursively perform message passing …
Collaborative FilteringContrastive LearningData AugmentationDenoising+2Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures
Federated recommender systems (FedRecs) have been widely explored recently due to their ability to protect user data privacy. In FedRecs, a central server collaboratively learns recommendation models by sharing model pub…
Collaborative FilteringModel PoisoningPrivacy PreservingRecommendation SystemsMulti-View Interactive Collaborative Filtering
In many scenarios, recommender system user interaction data such as clicks or ratings is sparse, and item turnover rates (e.g., new articles, job postings) high. Given this, the integration of contextual "side" informati…
ArticlesCollaborative FilteringRecommendation SystemsEye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis
Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking data to inform a collaborative filtering …
Collaborative FilteringMovie RecommendationRecommendation SystemsDo LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can comprehend user preferences based on their pre…
Collaborative FilteringWorld Knowledge