%0 Journal Article %1 noauthororeditor %A Moghaddam, Morteza Ghorbani %A Mustapha, Norwati %A Mustapha, Aida %A Sharef, Nurfadhlina Mohd %A Elahian, Anousheh %D 2015 %J Advanced Computational Intelligence: An International Journal (ACII) %K Collaborative E-Commerce Filtering Information Recommender Systems Trust metric overload %N 3 %P 12 %R 10.5121/acii.2015.2301 %T Trust Metrics In Recommender System : A Survey %U http://airccse.org/journal/acii/papers/2315acii01.pdf %V 2 %X Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of information that lead following the information flow in real world be impossible. Recommender systems, as the most successful application of information filtering, help users to find items of their interest from huge datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems focus on trustworthy value on relation among users to make more reliable and accurate recommends. In this paper our focus is on trust based approach and discuss about the process of making recommendation in these method. Furthermore, we review different proposed trust metrics, as the most important step in this process.

@article{noauthororeditor, abstract = {Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of information that lead following the information flow in real world be impossible. Recommender systems, as the most successful application of information filtering, help users to find items of their interest from huge datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems focus on trustworthy value on relation among users to make more reliable and accurate recommends. In this paper our focus is on trust based approach and discuss about the process of making recommendation in these method. Furthermore, we review different proposed trust metrics, as the most important step in this process. }, added-at = {2017-12-26T22:33:12.000+0100}, author = {Moghaddam, Morteza Ghorbani and Mustapha, Norwati and Mustapha, Aida and Sharef, Nurfadhlina Mohd and Elahian, Anousheh}, biburl = {https://www.bibsonomy.org/bibtex/20c77045cd48d340aefa3fa2ccf3ab7b0/janakirob}, doi = {10.5121/acii.2015.2301}, interhash = {c8e1f079fdd78e00671cd71705319101}, intrahash = {0c77045cd48d340aefa3fa2ccf3ab7b0}, issn = {2454 - 3934}, journal = {Advanced Computational Intelligence: An International Journal (ACII)}, keywords = {Collaborative E-Commerce Filtering Information Recommender Systems Trust metric overload}, language = {English}, month = {July}, number = 3, pages = 12, timestamp = {2017-12-26T22:33:12.000+0100}, title = {Trust Metrics In Recommender System : A Survey}, url = {http://airccse.org/journal/acii/papers/2315acii01.pdf}, volume = 2, year = 2015 }