Personalized Query Results using User Search Logs

Abstract

ABSTRACT:Most users want their search engine to incorporate three key features in query results. Relevant results (results they are actually interested in), Uncluttered (easy to read interface), Helpful options to broaden or tighten a search for accuracy. This paper addresses the third aspect with new improvement measures for an enhanced experience to the end user. A trivial query like purchasing a laptop has to be broken down into a number of co-dependent steps over a period of time based on prior search patterns of the same user .For instance, a user may first search for company and later the features and price. After deciding the company and price the user may then search for the accessories like mouse and modem etc. Each step requires more queries and each query completes with more clicks. Current search engines cannot support this kind of hierarchical queries. We propose to implement Random walk propagation methods that can construct user profiles based on the credentials obtained from their prior search history repositories. Combined with click points driven click graphs of user search behavior the IR system can support complex queries for future requests at reduced navigations. Random walk propagation over the query fusion graph methods support complex search quests in IR systems at reduced times. For developing an interactive IR system we also propose to use these search quests as auto complete features in similar query propagations. Biasing the ranking of search results can also be provided using ranking algorithms (top-k algorithms).Supporting these methods yields dynamic and improved performance in IR systems, by providing enriched user querying experience. A practical implementation of the proposed system validates our claim.