Article

Efficient Personalized Web Mining: Utilizing The Most Utilized Data

09/2011; DOI: 10.1007/978-3-642-22555-0_43
Source: arXiv

ABSTRACT Looking into the growth of information in the web it is a very tedious
process of getting the exact information the user is looking for. Many search
engines generate user profile related data listing. This paper involves one
such process where the rating is given to the link that the user is clicking
on. Rather than avoiding the uninterested links both interested links and the
uninterested links are listed. But sorted according to the weightings given to
each link by the number of visit made by the particular user and the amount of
time spent on the particular link.

0 Bookmarks
 · 
61 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Log files contain information about User Name, IP Address, Time Stamp, Access Request, number of Bytes Transferred, Result Status, URL that Referred and User Agent. The log files are maintained by the web servers. By analysing these log files gives a neat idea about the user. This paper gives a detailed discussion about these log files, their formats, their creation, access procedures, their uses, various algorithms used and the additional parameters that can be used in the log files which in turn gives way to an effective mining. It also provides the idea of creating an extended log file and learning the user behaviour.
    International Journal of Network Security & Its Applications. 01/2011;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: User profiling is a fundamental component of any personalization applications. Most existing user profiling strategies are based on objects that users are interested in (i.e., positive preferences), but not the objects that users dislike (i.e., negative preferences). In this paper, we focus on search engine personalization and develop several concept-based user profiling methods that are based on both positive and negative preferences. We evaluate the proposed methods against our previously proposed personalized query clustering method. Experimental results show that profiles which capture and utilize both of the user's positive and negative preferences perform the best. An important result from the experiments is that profiles with negative preferences can increase the separation between similar and dissimilar queries. The separation provides a clear threshold for an agglomerative clustering algorithm to terminate and improve the overall quality of the resulting query clusters.
    IEEE Transactions on Knowledge and Data Engineering 01/2010; 22(7):969-982. · 1.89 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Web-page recommendation is to predict the next request of pages that Web users are potentially interested in when surfing the Web. This technique can guide Web users to find more useful pages without asking for them explicitly and has attracted much attention in the community of Web mining. However, few studies on Web page recommendation consider personalization, which is an indispensable feature to meet various preferences of users. In this paper, we propose a personalized Web page recommendation model called PIGEON (abbr. for PersonalIzed web paGe rEcommendatiON) via collaborative filtering and a topic-aware Markov model. We propose a graph-based iteration algorithm to discover users' interested topics, based on which user similarities are measured. To recommend topically coherent pages, we propose a topic-aware Markov model to learn users' navigation patterns which capture both temporal and topical relevance of pages. A thorough experimental evaluation conducted on a large real dataset demonstrates PIGEON's effectiveness and efficiency.
    ICDM 2010, The 10th IEEE International Conference on Data Mining, Sydney, Australia, 14-17 December 2010; 01/2010

Full-text

View
3 Downloads
Available from