Papers Collaborative Filtering
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A Survey on Session-based Recommender Systems
Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based r…
Collaborative FilteringDecision MakingRecommendation SystemsSurveyWhen Collaborative Filtering Meets Reinforcement Learning
In this paper, we study a multi-step interactive recommendation problem, where the item recommended at current step may affect the quality of future recommendations. To address the problem, we develop a novel and effecti…
Collaborative FilteringInteractive Recommendationreinforcement-learningReinforcement Learning+1Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use…
BIG-bench Machine LearningCollaborative FilteringFederated LearningPrivacy PreservingMulti-Task Feature Learning for Knowledge Graph Enhanced Recommendation
Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the perfor…
Collaborative FilteringGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graphs+3Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to i…
Collaborative FilteringMovie RecommendationRecommendation SystemsTransfer LearningLearning Vertex Representations for Bipartite Networks
Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of…
Collaborative FilteringKnowledge GraphsNetwork EmbeddingRecommendation Systems+1DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System
In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it's almost impossible to directly match users and…
Collaborative FilteringRecommendation SystemsRepresentation LearningHybrid Recommender Systems: A Systematic Literature Review
Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation st…
Collaborative FilteringRecommendation SystemsSystematic Literature ReviewLarge-scale Collaborative Filtering with Product Embeddings
The application of machine learning techniques to large-scale personalized recommendation problems is a challenging task. Such systems must make sense of enormous amounts of implicit feedback in order to understand user …
Collaborative FilteringRepresentation LearningA Neural Network Based Explainable Recommender System
Recommendation system could help the companies to persuade users to visit or consume at a particular place, which was based on many traditional methods such as the set of collaborative filtering algorithms. Most research…
Collaborative FilteringFeature EngineeringPredictionRecommendation SystemsDeep Item-based Collaborative Filtering for Sparse Implicit Feedback
Recommender systems are ubiquitous in the domain of e-commerce, used to improve the user experience and to market inventory, thereby increasing revenue for the site. Techniques such as item-based collaborative filtering …
Collaborative FilteringRecommendation SystemsC-3PO: Click-sequence-aware DeeP Neural Network (DNN)-based Pop-uPs RecOmmendation
With the emergence of mobile and wearable devices, push notification becomes a powerful tool to connect and maintain the relationship with App users, but sending inappropriate or too many messages at the wrong time may r…
Collaborative FilteringUnifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews
This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product aspects), word sentiment and user preference as …
ClusteringCollaborative FilteringOnline Review Ratingregression+1Factorization Machines for Data with Implicit Feedback
In this work, we propose FM-Pair, an adaptation of Factorization Machines with a pairwise loss function, making them effective for datasets with implicit feedback. The optimization model in FM-Pair is based on the BPR (B…
Collaborative FilteringLearning-To-RankDeep Heterogeneous Autoencoders for Collaborative Filtering
This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems. We propose a model that learns a shared feature space from heterogeneous data, such as item descriptio…
Collaborative FilteringRecommendation SystemsExploiting Implicit Trust and Geo-social Network for Recommendation
Abstract—Recommender system (RS) seeks to predict the rating or preference a user would give to an item, this system often relies on collaborative filtering (CF). CF suffers from the problems of data sparsity, cold sta…
Collaborative FilteringRecommendation SystemsMOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank
Learning to rank has attracted increasing interest in the past decade, due to its wide applications in the areas like document retrieval and collaborative filtering. Feature selection for learning to rank is to select a…
Collaborative Filteringfeature selectionLearning-To-RankModeling Dynamic Missingness of Implicit Feedback for Recommendation
Implicit feedback is widely used in collaborative filtering methods for recommendation. It is well known that implicit feedback contains a large number of values that are \emph{missing not at random} (MNAR); and the miss…
Collaborative FilteringRecommendation SystemsMovie Recommendation System using Sentiment Analysis from Microblogging Data
Recommendation systems are important intelligent systems that play a vital role in providing selective information to users. Traditional approaches in recommendation systems include collaborative filtering and content-ba…
Collaborative FilteringMovie RecommendationRecommendation SystemsSentiment AnalysisRecommending Users: Whom to Follow on Federated Social Networks
To foster an active and engaged community, social networks employ recommendation algorithms that filter large amounts of contents and provide a user with personalized views of the network. Popular social networks such as…
Collaborative FilteringRecommendation Systems