Personalized Query Expansion for Web Search Using Social Keywords

A person is generally motivated by the thoughts of a set of people in his social network and he has different degree of interest in each of those people considering the common interest, trust, philosophy and several other factors between them. In this work, we model the social context of the person as the status messages generated by those socially associated people and propose a method to use his social context to improve the web search query expansion process for him. Our method extracts and ranks keywords from the status messages, which are relevant with the initial search query that is to be expanded. The selected keyword is then appended with the initial query to form socially expanded query. We show that useful search queries can be formed in terms of specialization and parallel movement, if we use the socially expanded query for further expansion using traditional expansion processes. Our method ensures privacy by keeping the social network data segregated from search engine vendors. Moreover, we provide directions for implementing this method without the intervention of search engine vendors. Nevertheless, the background search process is considered to be provided by search engine vendors in the form of Application Program Interface (API).