Web Page Prediction Based on Conditional Random Fields

Web page prefetching is used to reduce the access latency of the Internet. However, if most prefetched Web pages are not visited by the users in their subsequent accesses, the limited network bandwidth and server resources will not be used efficiently and even worsen the access delay problem. Therefore, enhancing theWeb page prediction accuracy is a main problem ofWeb page prefetching. Conditional Random Fields (CRFs), which are popular sequential learning models, have already been successfully used for many Natural Language Processing (NLP) tasks such as POS tagging, name entity recognition (NER) and segmentation. In this paper, we propose the use of CRFs in the field of Web page prediction. We treat the accessing sessions of previous Web users as observation sequences and label each element of these observation sequences to get the corresponding label sequences, then based on these observation and label sequences we use CRFs to train a prediction model and predict the probable subsequent Web pages for the current users. Our experimental results show that CRFs can produce higher Web page prediction accuracy effectively when compared with other popular techniques like plain Markov Chains and Hidden Markov Models (HMMs).