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Robust enhanced collaborative filtering without explicit noise filtering
Graph convolutional neural networks have been successfully applied to collaborative filtering to capture high-quality user-item representations....
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Method for Detecting Manipulation Attacks on Recommender Systems with Collaborative Filtering
Abstract —The security of recommendation systems with collaborative filtering from manipulation attacks is considered. The most common types of...
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Comparative analysis of collaborative filtering techniques for the multi-criteria recommender systems
Recommender systems are essential tools for many e-commerce services, such as Amazon, Netflix, etc. to recommend new items to users. Among various...
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Adaptive spectral graph wavelets for collaborative filtering
Collaborative filtering is a popular approach in recommender systems, whose objective is to provide personalized item suggestions to potential users...
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Non-pairwise Collaborative Filtering
On the users’ interaction graph, neighbors have been widely explored in the embedding function of collaborative filtering to address the sparsity...
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Collaborative Filtering
Collaborative filtering is a very popular method in recommendation engines. It is the predictive process behind the suggestions provided by these...
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A hybrid collaborative filtering mechanism for product recommendation system
The collaborative model is the needed framework to find a good product in both user- and budget-friendly. These collaborative filtering models have...
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Community-aware graph contrastive learning for collaborative filtering
Recently, graph neural networks have demonstrated superior performance in the field of collaborative filtering (CF). The graph collaborative...
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Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation
Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these...
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Residual Graph Convolution Collaborative Filtering with Asymmetric neighborhood aggregation
Due to the superior performance of graph convolutional networks (GCNs) in feature extraction and representation, researchers have introduced GCNs to...
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Hypergraph projection enhanced collaborative filtering
Collaborative filtering (CF) plays a vital role in recommendation scenarios, which models user-item interactions and learns user/item representations...
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Improving graph collaborative filtering with multimodal-side-information-enriched contrastive learning
The multimodal side information such as images and text have been commonly used as supplements to improve graph collaborative filtering...
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Deep learning-based collaborative filtering recommender systems: a comprehensive and systematic review
Nowadays, the volume of online information is growing and it is difficult to find the required information. Effective strategies such as recommender...
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Integrating user-side information into matrix factorization to address data sparsity of collaborative filtering
Recommendation techniques play a vital role in recommending an actual product to an intended user. The recommendation also supports the user in the...
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FairCF: fairness-aware collaborative filtering
Collaborative filtering (CF) techniques learn user and item embeddings from user-item interaction behaviors, and are commonly used in recommendation...
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A novel target item-based similarity function in privacy-preserving collaborative filtering
Memory-based collaborative filtering schemes are among the most effective recommendation technologies in terms of prediction quality, despite...
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Empowering neural collaborative filtering with contextual features for multimedia recommendation
A rapid growth in multimedia on various application platforms has made essential the provision of additional assistive technologies to handle...
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Social media reviews based hotel recommendation system using collaborative filtering and big data
To eliminate the concerns of cold-start and scalability within the filtering, collaborative recommendation system for a hotel under the ranking list...
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CoDFi-DL: a hybrid recommender system combining enhanced collaborative and demographic filtering based on deep learning
The cold start problem has always been a major challenge for recommender systems. It arises when the system lacks rating records for new users or...
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A Collaborative Filtering Recommendation Algorithm Based on Community Detection and Graph Neural Network
Recommendation system is an important module of many online systems. As one of the mainstream methods in the current recommendation system, the...
