Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
CARES: a ranking-oriented CADAL recommender system
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Using a trust network to improve top-N recommendation
Proceedings of the third ACM conference on Recommender systems
User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
Solving k-Nearest Neighbor Problem on Multiple Graphics Processors
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
| Hi-index | 0.00 |
A book recommender system is very useful for a digital library. Good book recommender systems can effectively help users find interesting and relevant books from the massive resources, by providing individual recommendation book list for each end-user. By now, a variety of collaborative filtering algorithms have been invented, which are the cores of most recommender systems. However, because of the explosion of information, especially in the Internet, the improvement of the efficiency of the collaborative filting (CF) algorithm becomes more and more important. In this paper, we first propose a parallel Top-N recommendation algorithm in CUDA (Compute Unified Device Architecture) which combines the collaborative filtering and trust-based approach to deal with the cold-start user problem. Then based on this algorithm, we present a parallel book recommender system on a GPU (Graphics Processor unit) for CADAL digital library platform. Our experimental results show our algorithm is very efficient to process the large-scale datasets with good accuracy, and we report the impact of different values of parameters on the recommendation performance.