The trust in trust-based recommender systems

سال انتشار: 1393
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 981

فایل این مقاله در 8 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

CCITC01_058

تاریخ نمایه سازی: 27 آبان 1393

چکیده مقاله:

the growth of e-commerce sites has posed a new challenge of information overload. It refers to the problem that caused by presence of too much information and resulted to difficult understanding and making decisions. Recommender systems help users to find the items of their interest from huge databases. Although collaborative filtering is the most successful technique for recommender systems, it suffers from several inherent issues such as data sparsity, cold start users, cold start items, low accuracy, and malicious attacks. To solve such issues, trust-based approaches have been proposed. These approaches use trustworthiness as a new factor to improve accuracy of recommendation especially in case of sparsity and cold-start users. This paper describes process of trust-based recommendation and carefully discusses about characteristics of trust, such as trust measurement metrics, visibility, value types, properties, dynamicity, propagation, etc. We also reviews the most important trust-based approaches

نویسندگان

Morteza Ghorbani Moghaddam

Faculty of Computer Science and IT University Putra Malaysia (UPM)

Anousheh Elahian

Faculty of Information Technology Virtual University of Shiraz Shiraz, Iran

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :
  • MLP, SVM and KNN for omparingه [22] E. Khadangi and ...
  • دانشگاه آزاد اسلامی واحد مرودشت- مهر ماه 1393 ...
  • Q. Yuan, L. Chen, Y. Liu, S. Ding, X. Zhang, ...
  • N. Lathia, S. Hailes, and L. Capra, Rrust-Based Collaborative Filtering, ...
  • _ _ _ 1052-1060, 2007. ...
  • Z. Fu-guo and X. Sheng-hua, Ropic-level Trust in Recommender Systems, ...
  • J. _ _ Golbeck, _ _ _ Proceedings of the ...
  • P. Massa and P Avesani, Rrust-aware recommender systems, " in ...
  • C. Ziegler, +AppleSeed) Towards decentralized recommender systems., " 2005. ...
  • _ _ _ _ _ _ _ ACM Trans Web, ...
  • _ _ _ _ _ _ 1773, Aug. 2013. ...
  • X. Cheng Chen, R. Jia Liu, and H. You Chang, ...
  • Application and System Modeling (ICCASM 2010), 2010, no. Iccasm, pp. ...
  • M. Ghorbani Moghaddam, N. Mustapha, A. Mustapha, and N. Mohd ...
  • _ _ _ _ _ _ _ Innovations in Information ...
  • M. Ghazanfar and A. Prigel-Bennett, =everaging clustering approaches to solve ...
  • P. Massa and P Avesani, rust metrics on controversial Asers: ...
  • R. Andersen, C. Borgs, J. Chayes, U. Feige, A. Flaxman, ...
  • J. O4Donovan and B. Smyth, +Profile-level Item-level)Trust in recommender systems, ...
  • _ _ _ _ recommenda tion, _ in Proceedings of ...
  • P. Massa and P Avesani, rust metrics on controversial sers: ...
  • _ _ _ Computing, 2013, pp. 340-346. ...
  • P. Bedi and R Sharma, +TARS)Trust based recommender system _ ...
  • _ _ _ _ Conference on Intelligent User Interface, 2009, ...
  • نمایش کامل مراجع