Implicitly Learning a User Interest Profile for Personalization of Web Search Using Collaborative Filtering

Abstract

The increasing abundance of content on the web has made information filtering even more important in helping users find information related to their interests. Personalization of web search is one such effort, that aims at improving the efficiency with which a user finds results relevant to his query. This is done by keeping track of a user's individual interests, and taking it into account while returning search results. We propose a robust user modeling technique that implicitly creates a Dynamic Category Interest Tree (DCIT), using a general ontology of the web and a set of web pages collected over time that give an insight into a user's interests. The DCIT is designed to use a fuzzy classification technique to keep track of what topics a user is interested in, his amount of interest in a topic, as well as reflect his changing interests overtime. The DCIT consists of a general ontology of the web, where each node represents a topic and consists of keywords that are usually used to describe that topic or category. Additional keywords that the user frequently associates with a topic, such as names of important people, organizations, or a specialized terminology, etc. Are also incorporated into the relevant topic. We use the Apriori Algorithm to extract these associated words from the user's web history in order to more accurately define the user's categories of interest. The DCIT is initially created by a content based approach using only the browsing history of the user, and is later further enhanced through collaborative filtering using the k-nearest neighbour-based algorithm. We propose a technique to re-rank the results from a search engine according to their relevance to a user, based on his implicitly learned DCIT. According to experimental results, our DCIT based ranking often outperforms search engines such as Google when it comes to retrieving web pages that are more relevant to a user's interest.