Proceedings ArticleDOI
Web Search Personalization by User Profiling
Mangesh Bedekar,Bharat M. Deshpande,Ramprasad Joshi +2 more
- pp 1099-1103
Reads0
Chats0
TLDR
The mathematics behind these 'link analysis algorithms' are analyzed and their effective use in ecommerce applications where they could be used for displaying 'personalized information' is analyzed.Abstract:
The World Wide Web is growing at a rate of about a million pages per day, making it tougher for search engines to extract relevant information for its users. Earlier Search Engines used simple indexing techniques to search for keywords in websites and gave more weightage to pages with higher frequency of keyword occurrences. This technique was easy to trick by using meta-tags liberally, claiming that their page used popular search terms, thereby, made meta-tags useless for search engines. Another technique widely used was to repeatedly use popular search terms in invisible text (white text on a white background) to fool engines. These fallacies called for a set of algorithms which would sort the results using an unbiased parameter. The currently employed Link Analysis Algorithms make use of the structure present in 'hyperlinks', sorted and displayed depending on a 'popularity index' decided to pages linking to it. In this work, we have analyzed the mathematics behind these 'link analysis algorithms' and their effective use in ecommerce applications where they could be used for displaying 'personalized information'.read more
Citations
More filters
Posted Content
User Profiling Trends, Techniques and Applications
TL;DR: The main objective of this paper is to explore the field of personalization in context of user profiling, to help researchers make aware of the user profiling.
Journal ArticleDOI
User Profiling for University Recommender System Using Automatic Information Retrieval
TL;DR: This paper aims at finding, extracting and integrating keyword based information from various web sources to generate a structured profile and does some experiments on the profiled information to generate knowledge out of it.
Proceedings ArticleDOI
Social network and user context assisted personalization for recommender systems
TL;DR: A new architecture for user personalization which combines both social network data and context data is designed which aggregates a user's preference data from various social networking services and then builds a centralized user profile which is accessible through public Web services.
Proceedings ArticleDOI
Personal search engine based on user interests and modified page rank
TL;DR: This paper introduces A Personal Search Engine which provides results relevant to the user's interest and depends on the degree of relevance of the document category to ensure relevant and accurate results.
References
More filters
Proceedings ArticleDOI
Scaling personalized web search
Glen Jeh,Jennifer Widom +1 more
TL;DR: The approach enables incremental computation, so that the construction of personalized views from partial vectors is practical at query time, and experimental results demonstrate the effectiveness and scalability of the techniques.
Book ChapterDOI
Integrating Web Usage and Content Mining for More Effective Personalization
TL;DR: This paper presents a framework for Web usage mining, distinguishing between the offine tasks of data preparation and mining, and the online process of customizing Web pages based on a user's active session, and describes effective techniques based on clustering to obtain a uniform representation for both site usage and site content profiles.
An Analytical Comparison of Approaches to Personalizing PageRank
TL;DR: This work analytically compares three recent approaches to personalizing PageRank and discusses the tradeoffs of each one.
Proceedings ArticleDOI
Notice of Violation of IEEE Publication Principles The Anatomy of a Large-Scale Hyper Textual Web Search Engine
TL;DR: Google as mentioned in this paper is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.