Survey on the objectives of recommender systems: Measures, solutions, evaluation methodology, and new perspectives
Recently, recommender systems have played an increasingly important role in a wide
variety of commercial applications to help users find favourite products. Research in the …
variety of commercial applications to help users find favourite products. Research in the …
Recommendation system based on deep sentiment analysis and matrix factorization
N Liu, J Zhao - IEEE Access, 2023 - ieeexplore.ieee.org
In order to solve the problem of data sparsity and credibility in collaborative filtering, a
recommendation system based on sentiment analysis and matrix factorization (SAMF) is …
recommendation system based on sentiment analysis and matrix factorization (SAMF) is …
Adaptive knn-based extended collaborative filtering recommendation services
In the current era of e-commerce, users are overwhelmed with countless products, making it
difficult to find relevant items. Recommendation systems generate suggestions based on …
difficult to find relevant items. Recommendation systems generate suggestions based on …
Collaborative filtering and kNN based recommendation to overcome cold start and sparsity issues: A comparative analysis
Collaborative Filtering (CF) has intrigued several researchers whose goal is to enhance
Recommender System's performance by mitigating their drawbacks. CF's common idea is to …
Recommender System's performance by mitigating their drawbacks. CF's common idea is to …
Multi-factor ranking method for trading-off accuracy, diversity, novelty, and coverage of recommender systems
Collaborative filtering (CF) is one of the most popular and commonly used recommendation
methods. Currently, most rating prediction CF methods select top-N recommendations …
methods. Currently, most rating prediction CF methods select top-N recommendations …
Towards comprehensive approaches for the rating prediction phase in memory-based collaborative filtering recommender systems
LNH Nam - 2022 - dl.acm.org
Recommender systems play an indispensable role in today's online businesses. In these
systems, memory-based (neighborhood-based) collaborative filtering is an important …
systems, memory-based (neighborhood-based) collaborative filtering is an important …
FoodRecNet: a comprehensively personalized food recommender system using deep neural networks
S Hamdollahi Oskouei, M Hashemzadeh - Knowledge and Information …, 2023 - Springer
Today, the huge variety of foods and the existence of different food preferences among
people have made it difficult to choose the right food according to people's food preferences …
people have made it difficult to choose the right food according to people's food preferences …
An intelligent system for multi-topic social spam detection in microblogging
The communication revolution has perpetually reshaped the means through which people
send and receive information. Social media is an important pillar of this revolution and has …
send and receive information. Social media is an important pillar of this revolution and has …
A new item-based collaborative filtering algorithm to improve the accuracy of prediction in sparse data
W Zhao, H Tian, Y Wu, Z Cui, T Feng - International Journal of …, 2022 - Springer
In memory-based collaborative filtering (CF) algorithms, the similarity and prediction method
have a significant impact on the recommendation results. Most of the existing …
have a significant impact on the recommendation results. Most of the existing …
ALCR: Adaptive loss based critic ranking toward variational autoencoders with multinomial likelihood and condition for collaborative filtering
J Feng, M Liu, X Liang, T Nie - Knowledge-Based Systems, 2023 - Elsevier
Research on variational autoencoders for collaborative filtering is gradually focusing on
implicit feedback. However, most existing studies have two limitations:(1) they overlook the …
implicit feedback. However, most existing studies have two limitations:(1) they overlook the …