Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Evaluating expertise recommendations
GROUP '01 Proceedings of the 2001 International ACM SIGGROUP Conference on Supporting Group Work
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
IEEE Transactions on Knowledge and Data Engineering
Investigating interactions of trust and interest similarity
Decision Support Systems
A survey of trust and reputation systems for online service provision
Decision Support Systems
Journal of Systems and Software
Key figure impact in trust-enhanced recommender systems
AI Communications - Recommender Systems
Content-Based Trust Mechanism for E-commerce Systems
APSCC '08 Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference
Multidimensional credibility model for neighbor selection in collaborative recommendation
Expert Systems with Applications: An International Journal
Avoiding Fake Neighborhoods in e-Commerce Collaborative Recommender Systems: A Semantic Approach
SMAP '09 Proceedings of the 2009 Fourth International Workshop on Semantic Media Adaptation and Personalization
A novel recommender system fusing the opinions from experts and ordinary people
Proceedings of the Workshop on Context-Aware Movie Recommendation
IEEE Transactions on Consumer Electronics
RESYGEN: A Recommendation System Generator using domain-based heuristics
Expert Systems with Applications: An International Journal
Collective intelligence as mechanism of medical diagnosis: The iPixel approach
Expert Systems with Applications: An International Journal
A static and dynamic recommendations system for best practice networks
HCI'13 Proceedings of the 15th international conference on Human-Computer Interaction: users and contexts of use - Volume Part III
The perception of others: inferring reputation from social media in the enterprise
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
| Hi-index | 12.05 |
Collaborative recommender systems select potentially interesting items for each user based on the preferences of like-minded individuals. Particularly, e-commerce has become a major domain in these research field due to its business interest, since identifying the products the users may like or find useful can boost consumption. During the last years, a great number of works in the literature have focused in the improvement of these tools. Expertise, trust and reputation models are incorporated in collaborative recommender systems to increase their accuracy and reliability. However, current approaches require extra data from the users that is not often available. In this paper, we present two contributions that apply a semantic approach to improve recommendation results transparently to the users. On the one hand, we automatically build implicit trust networks in order to incorporate trust and reputation in the selection of the set of like-minded users that will drive the recommendation. On the other hand, we propose a measure of practical expertise by exploiting the data available in any e-commerce recommender system - the consumption histories of the users.