ACM SIGKDD Explorations Newsletter
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
The web changes everything: understanding the dynamics of web content
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A New Information Filtering Method for WebPages
DEXA '10 Proceedings of the 2010 Workshops on Database and Expert Systems Applications
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In the Internet era, information processing for personalization and relevance has been one of the key topics of research and development. It ranges from design of applications like search engines, web crawlers, learning engines to reverse image searches, audio processed search, auto complete, etc. Information retrieval plays a vital role in most of the above mentioned applications. A part of information retrieval which deals with personalization and rendering is often referred to as Information Filtering. The emphasis of this paper is to empirically analyze the information filters commonly seen and to analyze their correctness and effects. The measure of correctness is not in terms of percentage of correct results but instead a rational approach of analysis using a non mathematical argument is presented. Filters employed by Google's search engine are used to analyse the effects of filtering on the web. A plausible solution to the errors of filtering phenomenon is also discussed.