Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
PHOAKS: a system for sharing recommendations
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
Investigating interactions of trust and interest similarity
Decision Support Systems
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
A classification of trust systems
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part I
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Knowledge-Based Systems
Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust
Expert Systems with Applications: An International Journal
Exploiting two-faceted web of trust for enhanced-quality recommendations
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Collaborative Filtering (CF) technique has proven to be promising for implementing large scale recommender systems but its success depends mainly on locating similar neighbors. Due to data sparsity of the user-item rating matrix, the process of finding similar neighbors does not often succeed. In addition to this, it also suffers from the new user (cold start) problem as finding possible neighborhood and giving recommendations to user who has not rated any item or rated very few items is difficult. In this paper, our proposed Trust based Ant Recommender System (TARS) produces valuable recommendations by incorporating a notion of dynamic trust between users and selecting a small and best neighborhood based on biological metaphor of ant colonies. Along with the predicted ratings, displaying additional information for explanation of recommendations regarding the strength and level of connectedness in trust graph from where recommendations are generated, items and number of neighbors involved in predicting ratings can help active user make better decisions. Also, new users can highly benefit from pheromone updating strategy known from ant algorithms as positive feedback in the form of aggregated dynamic trust pheromone defines ''popularity'' of a user as recommender over a period of time. The performance of TARS is evaluated using two datasets of different sparsity levels viz. Jester dataset and MovieLens dataset (available online) and compared with traditional Collaborative Filtering based approach for generating recommendations.