THE PERSONALIZED PAGERANK BASED ON USER BEHAVIORS

摘要

The PageRank algorithm takes advantage of the link structure of the Web to improve the ranking of query results, independent of any particular users. Obviously, the PageRank score reflects a "democratic" importance that has no preference for any particular pages. In fact, users always browse those Web pages in which they have more interest. So, we propose a new personalized ranking vector--UserRank (UR) based on user behaviors of browsing pages to estimate the importance of Web pages. In order to yield more accurate query results, we take the UR vector combined with PageRank vector to generate a new ranking vector--Personalized PageRank (P~2R). Experimental results demonstrate that our P~2R algorithm can more closely reflect user preference and more largely improve the precision of ranking results.