Papers Collaborative Filtering
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Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety of recommendation algorithms, is referred…
Collaborative FilteringMusic RecommendationRecommendation SystemsGraph Trend Filtering Networks for Recommendations
Recommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives. The key of recommender systems is to predict how likely users will interact with items …
Collaborative FilteringGraph Representation LearningRecommendation SystemsRepresentation LearningLocalized Graph Collaborative Filtering
User-item interactions in recommendations can be naturally de-noted as a user-item bipartite graph. Given the success of graph neural networks (GNNs) in graph representation learning, GNN-based C methods have been propos…
Collaborative FilteringGraph Representation LearningRecommendation SystemsRepresentation LearningLT-OCF: Learnable-Time ODE-based Collaborative Filtering
Collaborative filtering (CF) is a long-standing problem of recommender systems. Many novel methods have been proposed, ranging from classical matrix factorization to recent graph convolutional network-based approaches. A…
Collaborative FilteringRecommendation SystemsA Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data
Recommender systems(RS), especially collaborative filtering(CF) based RS, has been playing an important role in many e-commerce applications. As the information being searched over the internet is rapidly increasing, use…
Collaborative FilteringRecommendation SystemsTransfer LearningUnsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering
We pioneer the first extractive summarization-based collaborative filtering model called ESCOFILT. Our proposed model specifically produces extractive summaries for each item and user. Unlike other types of explanations,…
Collaborative FilteringExtractive SummarizationSentenceSentence Embeddings+1Sparse Feature Factorization for Recommender Systems with Knowledge Graphs
Deep Learning and factorization-based collaborative filtering recommendation models have undoubtedly dominated the scene of recommender systems in recent years. However, despite their outstanding performance, these metho…
Collaborative FilteringKnowledge GraphsRecommendation SystemsReenvisioning Collaborative Filtering vs Matrix Factorization
Collaborative filtering models based on matrix factorization and learned similarities using Artificial Neural Networks (ANNs) have gained significant attention in recent years. This is, in part, because ANNs have demonst…
Collaborative FilteringDiversityDeep Variational Models for Collaborative Filtering-based Recommender Systems
Deep learning provides accurate collaborative filtering models to improve recommender system results. Deep matrix factorization and their related collaborative neural networks are the state-of-art in the field; neverthel…
Collaborative FilteringData AugmentationRecommendation SystemsHierarchical Latent Relation Modeling for Collaborative Metric Learning
Collaborative Metric Learning (CML) recently emerged as a powerful paradigm for recommendation based on implicit feedback collaborative filtering. However, standard CML methods learn fixed user and item representations, …
Collaborative FilteringGraph EmbeddingKnowledge Graph EmbeddingMetric Learning+2Content-driven Music Recommendation: Evolution, State of the Art, and Challenges
The music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collaborative filtering (CF) techniques, music …
ArticlesCollaborative FilteringMusic RecommendationRecommendation SystemsRecommending best course of treatment based on similarities of prognostic markers
With the advancement in the technology sector spanning over every field, a huge influx of information is inevitable. Among all the opportunities that the advancements in the technology have brought, one of them is to pro…
Collaborative FilteringRecommendation SystemsRetrievalLearning to Recommend Items to Wikidata Editors
Wikidata is an open knowledge graph built by a global community of volunteers. As it advances in scale, it faces substantial challenges around editor engagement. These challenges are in terms of both attracting new edito…
Collaborative FilteringRecommendation SystemsINMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering
Collaborative filtering is one of the most common scenarios and popular research topics in recommender systems. Among existing methods, latent factor models, i.e., learning a specific embedding for each user/item by reco…
Collaborative FilteringRecommendation SystemsDetails Preserving Deep Collaborative Filtering-Based Method for Image Denoising
In spite of the improvements achieved by the several denoising algorithms over the years, many of them still fail at preserving the fine details of the image after denoising. This is as a result of the smooth-out effect …
Collaborative FilteringDenoisingImage DenoisingSSIMSVP-CF: Selection via Proxy for Collaborative Filtering Data
We study the practical consequences of dataset sampling strategies on the performance of recommendation algorithms. Recommender systems are generally trained and evaluated on samples of larger datasets. Samples are often…
Collaborative FilteringRecommendation SystemsCollaborative Filtering-Based Method for Low-Resolution and Details Preserving Image Denoising
Over the years, progressive improvements in denoising performance have been achieved by several image denoising algorithms that have been proposed. Despite this, many of these state-of-the-art algorithms tend to smooth o…
Collaborative FilteringDenoisingImage DenoisingSelfCF: A Simple Framework for Self-supervised Collaborative Filtering
Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different …
Collaborative FilteringSelf-Supervised LearningThe Stereotyping Problem in Collaboratively Filtered Recommender Systems
Recommender systems play a crucial role in mediating our access to online information. We show that such algorithms induce a particular kind of stereotyping: if preferences for a set of items are anti-correlated in the g…
Collaborative FilteringRecommendation SystemsBanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System
Multi-armed bandits (MAB) provide a principled online learning approach to attain the balance between exploration and exploitation. Due to the superior performance and low feedback learning without the learning to act in…
Collaborative FilteringMulti-Armed BanditsRecommendation Systemsvalid