Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/1998076.1998131 | JCDL |
Keywords | Field | DocType |
parallel top-n recommendation algorithm,collaborative filting,cadal digital library platform,good book recommender system,book recommender system,social network-aware top-n recommender,recommender system,parallel book recommender system,relevant book,individual recommendation book list,recommendation performance,social network,collaborative filtering,digital library | Recommender system,Architecture,Graphical processing unit,Social network,Collaborative filtering,Information retrieval,CUDA,Computer science,Digital library,Multimedia,The Internet | Conference |
Citations | PageRank | References |
4 | 0.41 | 16 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruifeng Li | 1 | 10 | 0.95 |
| Yin Zhang | 2 | 3492 | 281.04 |
| Haihan Yu | 3 | 12 | 1.32 |
| Xiaojun Wang | 4 | 10 | 0.95 |
| Jiang-qin Wu | 5 | 235 | 26.47 |
| baogang | 6 | 209 | 29.51 |