Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Wasserstein Autoencoders for Collaborative Filtering
The recommender systems have long been investigated in the literature. Recently, users' implicit feedback like `click' or `browse' are considered to be able to enhance the recommendation performance. Therefore, a number …
Collaborative FilteringRecommendation SystemsLensKit for Python: Next-Generation Software for Recommender System Experiments
LensKit is an open-source toolkit for building, researching, and learning about recommender systems. First released in 2010 as a Java framework, it has supported diverse published research, small-scale production deploym…
Collaborative FilteringRecommendation SystemsAction-conditional Sequence Modeling for Recommendation
In many online applications interactions between a user and a web-service are organized in a sequential way, e.g., user browsing an e-commerce website. In this setting, recommendation system acts throughout user navigati…
Collaborative FilteringRecommendation SystemsHyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic space and collaborative filtering by explor…
Collaborative FilteringMetric LearningRecommendation SystemsRepresentation LearningA Novel Deterministic Framework for Non-probabilistic Recommender Systems
Recommendation is a technique which helps and suggests a user, any relevant item from a large information space. Current techniques for this purpose include non-probabilistic methods like content-based filtering an…
Bayesian InferenceCollaborative FilteringRecommendation SystemsA novel graph-based model for hybrid recommendations in cold-start scenarios
Cold-start is a very common and still open problem in the Recommender Systems literature. Since cold start items do not have any interaction, collaborative algorithms are not applicable. One of the main strategies is to …
BIG-bench Machine LearningCollaborative FilteringInformation RetrievalRecommendation Systems+1Aesthetic Features for Personalized Photo Recommendation
Many photography websites such as Flickr, 500px, Unsplash, and Adobe Behance are used by amateur and professional photography enthusiasts. Unlike content-based image search, such users of photography websites are not jus…
Collaborative FilteringImage RetrievalStyle TransferEigenvalue analogy for confidence estimation in item-based recommender systems
Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of the conditions under which item-based r…
Collaborative FilteringRecommendation SystemsTowards Large Scale Training Of Autoencoders For Collaborative Filtering
In this paper, we apply a mini-batch based negative sampling method to efficiently train a latent factor autoencoder model on large scale and sparse data for implicit feedback collaborative filtering. We compare our work…
Collaborative FilteringSpectral Collaborative Filtering
Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the \textit{cold-start} problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In t…
Collaborative FilteringRecommendation SystemsUsing Taste Groups for Collaborative Filtering
Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and domain independent. However, there is a lack of negative examples. Existing works circumvent thi…
Collaborative FilteringSuperhighway: Bypass Data Sparsity in Cross-Domain CF
Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains. Many traditional methods focus on enriching compared neighborhood re…
Collaborative FilteringExploring Author Gender in Book Rating and Recommendation
Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of the patterns in rating datasets reflect important real-world differe…
Collaborative FilteringCoBaR: Confidence-Based Recommender
Neighborhood-based collaborative filtering algorithms usually adopt a fixed neighborhood size for every user or item, although groups of users or items may have different lengths depending on users' preferences. In this …
ClusteringCollaborative FilteringNeighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations
In this paper, we present work-in-progress on applying user pre-filtering to speed up and enhance recommendations based on Collaborative Filtering. We propose to pre-filter users in order to extract a smaller set of cand…
Collaborative FilteringRecommendation SystemsNeural Collaborative Ranking
Recommender systems are aimed at generating a personalized ranked list of items that an end user might be interested in. With the unprecedented success of deep learning in computer vision and speech recognition, recently…
Collaborative FilteringCollaborative RankingRecommendation Systemsspeech-recognition+1Outer Product-based Neural Collaborative Filtering
In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimen…
Collaborative FilteringMulti-Domain Recommender SystemsRecommendation SystemsMatrix Factorization on GPUs with Memory Optimization and Approximate Computing
Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative filtering, data compression, feature extraction, word embedding, etc. While many proble…
Collaborative FilteringCPUData CompressionGPUOBOE: Collaborative Filtering for AutoML Model Selection
Algorithm selection and hyperparameter tuning remain two of the most challenging tasks in machine learning. Automated machine learning (AutoML) seeks to automate these tasks to enable widespread use of machine learning b…
Active LearningAutoMLBIG-bench Machine LearningCollaborative Filtering+3A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care
We partner with a leading European healthcare provider and design a mechanism to match patients with family doctors in primary care. We define the matchmaking process for several distinct use cases given different levels…
Collaborative FilteringRecommendation Systems