Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Trust network analysis with subjective logic
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
Investigating interactions of trust and interest similarity
Decision Support Systems
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Optimal Trust Network Analysis with Subjective Logic
SECURWARE '08 Proceedings of the 2008 Second International Conference on Emerging Security Information, Systems and Technologies
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
Personalized Recommender Systems Integrating Social Tags and Item Taxonomy
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A paradox for trust and reputation in the e-commerce world
ACSC '13 Proceedings of the Thirty-Sixth Australasian Computer Science Conference - Volume 135
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Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its performance is promising enough. But research shows that it performs very poor in a cold start situation where previous rating data is sparse. As an alternative, trust can be used for neighbor formation to generate automated recommendation. User assigned explicit trust rating such as how much they trust each other is used for this purpose. However, reliable explicit trust data is not always available. In this paper we propose a new method of developing trust networks based on user's interest similarity in the absence of explicit trust data. To identify the interest similarity, we have used user's personalized tagging information. This trust network can be used to find the neighbors to make automated recommendations. Our experiment result shows that the proposed trust based method outperforms the traditional collaborative filtering approach which uses users rating data. Its performance improves even further when we utilize trust propagation techniques to broaden the range of neighborhood.