A Cluster Based Multidimensional Ontology Mining For Personalized Search

As the amount of Web information grows rapidly, search engines must be able to retrieve information according to the user's preference. In this project, we propose a new web search personalization approach that captures the user's interests and preferences in the form of concepts by mining search results and their clickthroughs. Due to the important role location information plays in web search, we separate concepts into content concepts and location concepts, and organize them into ontologies to create an ontology-based user profile to precisely capture the user's content and location interests and hence improve the search accuracy. Moreover, recognizing the fact that different users and queries may have different emphases on content and location information, the users are clustered into two classes using K-Means based on content and locations. Ranking SVM is employed in our personalization approach to learn the user's preferences. For a given query, a set of content concepts and a set of location concepts are extracted from the search result as the document features. Since each document can be represented by a feature vector, it can be treated as a point in the feature space. Using clickthrough data as the input, RSVM aims at finding a linear ranking function, which holds for as many document preference pairs as possible.