Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
Recently, recommender systems play a pivotal role in alleviating the problem of information overload. Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the i…
Collaborative FilteringRecommendation SystemsTime-weighted Attentional Session-Aware Recommender System
Session-based Recurrent Neural Networks (RNNs) are gaining increasing popularity for recommendation task, due to the high autocorrelation of user's behavior on the latest session and the effectiveness of RNN to capture t…
Collaborative FilteringRecommendation SystemsWasserstein Collaborative Filtering for Item Cold-start Recommendation
The item cold-start problem seriously limits the recommendation performance of Collaborative Filtering (CF) methods when new items have either none or very little interactions. To solve this issue, many modern Internet a…
Collaborative FilteringPMD: An Optimal Transportation-based User Distance for Recommender Systems
Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users. As a result, these measures cannot fully utilize the rating information and are…
Collaborative FilteringRecommendation SystemsDeep Context-Aware Recommender System Utilizing Sequential Latent Context
Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challenging, since the addition of context may i…
Collaborative FilteringDecoderRecommendation SystemsBoosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks
Item-based models are among the most popular collaborative filtering approaches for building recommender systems. Random walks can provide a powerful tool for harvesting the rich network of interactions captured within t…
Collaborative FilteringRecommendation SystemsAssessing Fashion Recommendations: A Multifaceted Offline Evaluation Approach
Fashion is a unique domain for developing recommender systems (RS). Personalization is critical to fashion users. As a result, highly accurate recommendations are not sufficient unless they are also specific to users. Mo…
Collaborative FilteringRecommendation SystemsA Compositional Model of Multi-faceted Trust for Personalized Item Recommendation
Trust-based recommender systems improve rating prediction with respect to Collaborative Filtering by leveraging the additional information provided by a trust network among users to deal with the cold start problem. Howe…
Collaborative FilteringRecommendation SystemsAlgorithms and System Architecture for Immediate Personalized News Recommendations
Personalization plays an important role in many services, just as news does. Many studies have examined news personalization algorithms, but few have considered practical environments. This paper provides algorithms and …
ArticlesCollaborative FilteringSDM: Sequential Deep Matching Model for Online Large-scale Recommender System
Capturing users' precise preferences is a fundamental problem in large-scale recommender system. Currently, item-based Collaborative Filtering (CF) methods are common matching approaches in industry. However, they are no…
Collaborative FilteringRecommendation SystemsHeterogeneous Collaborative Filtering
Recommendation system is important to a content sharing/creating social network. Collaborative filtering is a widely-adopted technology in conventional recommenders, which is based on similarity between positively engage…
Collaborative FilteringDiversitySession-based Complementary Fashion Recommendations
In modern fashion e-commerce platforms, where customers can browse thousands to millions of products, recommender systems are useful tools to navigate and narrow down the vast assortment. In this scenario, complementary …
Collaborative FilteringNavigateRecommendation SystemsTwo-Stage Session-based Recommendations with Candidate Rank Embeddings
Recent advances in Session-based recommender systems have gained attention due to their potential of providing real-time personalized recommendations with high recall, especially when compared to traditional methods like…
Collaborative FilteringRecommendation SystemsRe-RankingSession-Based Recommendations+1Assessing the Impact of a User-Item Collaborative Attack on Class of Users
Collaborative Filtering (CF) models lie at the core of most recommendation systems due to their state-of-the-art accuracy. They are commonly adopted in e-commerce and online services for their impact on sales volume and/…
Collaborative FilteringRecommendation SystemsLatent User Linking for Collaborative Cross Domain Recommendation
With the widespread adoption of information systems, recommender systems are widely used for better user experience. Collaborative filtering is a popular approach in implementing recommender systems. Yet, collaborative f…
Collaborative FilteringRecommendation SystemsTransfer LearningA Novel Kalman Filter Based Shilling Attack Detection Algorithm
Collaborative filtering has been widely used in recommendation systems to recommend items that users might like. However, collaborative filtering based recommendation systems are vulnerable to shilling attacks. Malicious…
Collaborative FilteringRecommendation SystemsFCNHSMRA_HRS: Improve the performance of the movie hybrid recommender system using resource allocation approach
Recommender systems are systems that are capable of offering the most suitable services and products to users. Through specific methods and techniques, the recommender systems try to identify the most appropriate items, …
ClusteringCollaborative FilteringRecommendation SystemsCUPCF: Combining Users Preferences in Collaborative Filtering for Better Recommendation
How to make the best decision between the opinions and tastes of your friends and acquaintances? Therefore, recommender systems are used to solve such issues. The common algorithms use a similarity measure to predict act…
Collaborative FilteringRecommendation SystemsUsing the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets
This work addresses the problem of providing and evaluating recommendations in data markets. Since most of the research in recommender systems is focused on the bipartite relationship between users and items (e.g., movie…
Collaborative FilteringRecommendation SystemsCross-Domain Collaborative Filtering via Translation-based Learning
With the proliferation of social media platforms and e-commerce sites, several cross-domain collaborative filtering strategies have been recently introduced to transfer the knowledge of user preferences across domains. T…
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