Next page prediction with popularity based page rank, duration based page rank and semantic tagging approach

Yanık, Banu Deniz
Using page rank and semantic information are frequently used techniques in next page prediction systems. In our work, we extend the use of Page Rank algorithm for next page prediction with several navigational attributes, which are size of the page, duration of the page visit and duration of transition (two page visits sequentially), frequency of page and transition. In our model, we define popularity of transitions and pages by using duration information, use it in a relation with page size, and visit frequency factors. By using the popularity value of pages, we bias conventional Page Rank algorithm and model a next page prediction system that produces page recommendations under given top-n value. Moreover, we extract semantic terms from web URLs in order to tag pages semantically. The extracted terms are mapped into web URLs with different level of details in order to find semantically similar pages for next page recommendations. With this tagging, we model another next page prediction method, which uses Semantic Tagging (ST) similarity and exploits PPR values as a supportive method. Moreover, we model a Hybrid Page Rank (HPR) algorithm that uses both Semantic Tagging based approach and Popularity Based Page Rank values of pages together in order to investigate the effect of PPR and ST with equal weights. In addition, we investigate the effect of local (a synopsis of directed web graph) and global (whole directed web graph) modeling on next page prediction accuracy.