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Context-aware document recommendation by mining sequential access data
| Content Provider | ACM Digital Library |
|---|---|
| Author | Wang, Wei Yu, Tao Chen, Tao Chen, Jidong Guo, Hang |
| Abstract | We propose a context-aware method for document recommendation. The idea is to model the historic sequential access data using a Variable Memory Markov (VMM) Model offline, and when online, make recommendation by searching a Predict Suffix Tree (PST). We implement a disk-based PST. Document recommendation is more challenging than web query recommendation due to the sparsity problem caused by larger state space. In the paper, we tackle the problem by (1) pruning in the modeling phase and (2) smoothing in the recommendation phase. Empirical evidence shows that our method can reduce the model complexity significantly and achieve good performance in recommendation accuracy. |
| Starting Page | 1 |
| Ending Page | 7 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781450315531 |
| DOI | 10.1145/2346604.2346612 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-08-12 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
