A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks
R Chen, Q Hua, YS Chang, B Wang, L Zhang… - IEEE …, 2018 - ieeexplore.ieee.org
In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
A survey of recommendation systems
Today's internet is able to discover almost any product or piece of information. The large
amounts of unfiltered information returned by an internet query calls for filters able to …
amounts of unfiltered information returned by an internet query calls for filters able to …
Reviewer credibility and sentiment analysis based user profile modelling for online product recommendation
Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for
humans, making its automation a very complex job. This research work augments heuristic …
humans, making its automation a very complex job. This research work augments heuristic …
Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering
J Chen, C Zhao, Uliji, L Chen - Complex & Intelligent Systems, 2020 - Springer
In recent years, application of recommendation algorithm in real life such as Amazon,
Taobao is getting universal, but it is not perfect yet. A few problems need to be solved such …
Taobao is getting universal, but it is not perfect yet. A few problems need to be solved such …
A new collaborative filtering recommendation method based on transductive SVM and active learning
X Wang, Z Dai, H Li, J Yang - Discrete Dynamics in Nature and …, 2020 - Wiley Online Library
In the collaborative filtering (CF) recommendation applications, the sparsity of user rating
data, the effectiveness of cold start, the strategy of item information neglection, and user …
data, the effectiveness of cold start, the strategy of item information neglection, and user …
A temporal clustering approach for social recommender systems
Recommender systems aim to suggest relevant items to users among a large number of
available items. They have been successfully applied in various industries, such as e …
available items. They have been successfully applied in various industries, such as e …
Scalable and data-independent multi-agent recommender system using social networks analysis
A Nazari, M Kordabadi… - International Journal of …, 2024 - World Scientific
Nowadays, many online users find the selection of information and required products
challenging due to the growing volume of data on the web. Recommender systems are …
challenging due to the growing volume of data on the web. Recommender systems are …
Clustering-based collaborative filtering using an incentivized/penalized user model
Giving or recommending appropriate content based on the quality of experience is the most
important and challenging issue in recommender systems. As collaborative filtering (CF) is …
important and challenging issue in recommender systems. As collaborative filtering (CF) is …
Tailoring recommendations to groups of viewers on smart TV: a real-time profile generation approach
The recommender systems predict and calculate user preferences for recommendations.
However, such predictions and calculations are neither accurate nor viable in the context of …
However, such predictions and calculations are neither accurate nor viable in the context of …
An effective user clustering-based collaborative filtering recommender system with grey wolf optimisation
N Sivaramakrishnan… - … Journal of Bio …, 2020 - inderscienceonline.com
The enormous amount of data available today often makes it difficult for users to make
decisions. Recommendation systems have become increasingly popular and mainly used in …
decisions. Recommendation systems have become increasingly popular and mainly used in …