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Neural Collaborative Filtering [PDF]

Proceedings of the 26th International Conference on World Wide Web, 2017
In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny.
Bordes A., den Oord A. Van, Glorot X., Kingma D., Rendle S., Salakhutdinov R., Socher R., Srivastava N., Strub F., Truyen T. T., Zheng Y.   +10 more
core   +3 more sources

Bias-corrected-based collaborative filtering recommendation (Bias-Corr-CF). [PDF]

PLoS ONE
The goal of the collaborative filtering problem is to find accurate and efficient mappings from previously rated data at items of the users. Improving item-based collaborative filtering (IBCF) and user-based collaborative filtering (UBCF) involves ...
Tu Cam Thi Tran, Hiep Xuan Huynh
doaj   +2 more sources

Dual-level graph contrastive collaborative filtering [PDF]

Scientific Reports
The latest research positions graph-based collaborative filtering as an effective strategy in recommendation systems, enabling the analysis of user preferences via user-item interaction graphs.
Jiahao Wang, Qingshuai Wang, Kai Ma, Noor Farizah Ibrahim, Zurinahni Zainol   +4 more
doaj   +2 more sources

Attention based collaborative filtering [PDF]

Известия Саратовского университета. Новая серия: Математика. Механика. Информатика, 2022
Attention mechanism invention was an important milestone in the development of the Natural Language Processing domain. It found many applications in different fields, like churn prediction, computer vision, speech recognition, and so on.
Romanov, Aleksey I., Batraeva, Inna A.
doaj   +1 more source

Improving Collaborative Filtering Recommender System Results and Performance using Combination of Fuzzy Grey Wolf Optimizer Algorithm and Lion Optimization Algorithm [PDF]

چشم‌انداز مدیریت صنعتی, 2021
Nowadays, recommender systems have reshaped the ways of information filtering between websites and the users in order to identify the users’ interests and generate product suggestions for the active users.
Zahra Nakhaei Rad, Hessam Zandhessami, Abbas Tolouei Ashlaghi   +2 more
doaj   +1 more source

Effects of Binary Vectors Similarities on the Accuracy of Multi-Criteria Collaborative Filtering

Sakarya University Journal of Computer and Information Sciences, 2021
Recommender systems offer tailored recommendations by employing various algorithms, and collaborative filtering is one of the well-known and commonly used of those.
Burcu Demirelli Okkalıoğlu
doaj   +1 more source

A collaborative filtering recommendation framework utilizing social networks

Machine Learning with Applications, 2023
Collaborative filtering is a widely used technique for providing personalized recommendations to users. However, traditional collaborative filtering methods fail to consider the social connections between users. The current study proposes a collaborative
Aamir Fareed, Saima Hassan, Samir Brahim Belhaouari, Zahid Halim   +3 more
doaj   +1 more source

Hybrid Recommendation Using Temporal Data for Accuracy Improvement in Item Recommendation

Journal of Information and Organizational Sciences, 2021
Recommender systems have become a vital entity to the business world in form of software tools to make decisions. It estimates the overloaded information and provides the suitable decisions in any kind of business work through online.
Desabandhu Parasuraman, Sathiyamoorthy Elumalai   +1 more
doaj   +1 more source

Spectral collaborative filtering [PDF]

Proceedings of the 12th ACM Conference on Recommender Systems, 2018
Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the \textit{cold-start} problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph.
Zheng, Lei, Lu, Chun-Ta, Jiang, Fei, Zhang, Jiawei, Yu, Philip S.   +4 more
openaire   +2 more sources

On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering Datasets

Information, 2022
One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item.
Dimitris Spiliotopoulos, Dionisis Margaris, Costas Vassilakis   +2 more
doaj   +1 more source