Friends, Romans, Countrymen: Lend me your URLs. Using Social Chatter to Personalize Web Search

摘要

People often find useful content on the web via social media. However, it is difficult for users to aggregate the information and recommendations embedded in a torrent of social feeds like email and Twitter. At the same time, the ever-growing size of the web and attempts to spam commercial search engines make it a challenge for users 10 get search results relevant to their unique background and interests. To address this problem, we propose ways to let users mine their own social chatter and extract people, pages and sites of potential interest. This information can be used to effectively personalize their web search results. Our approach has the benefits of generating personalized and socially eurated results, removing web spam and preserving user privacy. We have built a system called Slant io automatically mine a user's email and Twitter feeds and populate four personalized search indices that are used to augment regular web search. We evaluated these indices with users and found that the small slice of the web indexed using social chatter can produce results that are equally or belter liked by users coin-pared to personalized search by a commercial search engine. We find that user satisfaction with search results can be improved by combining the best results from multiple indices.