Towards Networks of Search Engines and Other Digital Experts: A Distributed Intelligence Approach

We outline our vision and high-level architecture of a network of "digital experts" such as online recommendation systems, general-and special-purpose search engines. Instead of choosing between e.g. Google, Bing and Baidu, a user may want to get integrated results from all three search engines (and more) via a single search query. The user may also want results or suggestions to questions implied by, but not explicitly stated, in his query. Ideally, the combined results from different search engines will be relevance-ranked, taking into account each user's individual preferences. There exist "expert systems" that integrate results or recommendations from multiple different websites and/or other search engines - e.g., the meta-search engines for finding the best flights and airfares. However, these meta-search engines do not (i) relevance-rank on behalf of the end-user, (ii) learn over time, which websites/individual search engines are most trustworthy and relevant to a particular user, (iii) maintain a quality assurance model of the individual sources of information or recommendation that they harvest, or (iv) create sub-queries or new queries based on inference of the user intent, and not merely what the user has explicitly asked for. We propose a unified framework to address all these issues. In particular, our goal is to enable the end-user to seamlessly obtain integrated expertise from a variety of sources, so that those recommendations are ranked based on both (a) the user's preferences and (b) different individual sources' of recommendation reputation and trustworthiness. Our vision for achieving these goals is to have a decentralized, transparent market-place of search engines, recommenders and knowledge bases, where the burdens of integrating, ranking and evaluating quality of different knowledge sources are taken off the end-user.