Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine

Effective sharing of diverse social media is often inhibi ted by limitations in their search and discovery mech-anisms, which are particularly restrictive for media that do not lend themselves to automatic processing orindexing. Here, we present the structure and mechanism of an adaptive search engine which is designed toovercome such limitations. The basic framework of the adaptive search engine is to capture human judg-ment in the course of normal usage from user queries in order to develop semantic indexes which link searchterms to media objects semantics. This approach is partic ularly effective for the retrieval of multimedia ob-jects, such as images, sounds, and videos, where a direct analysis of the object features does not allow them tobe linked to search terms, for example, nontextual/icon-based search, deep semantic search, or when searchterms are unknown at the time the media repository is built. An adaptive search architecture is presentedto enable the index to evolve with respect to user feedback, while a randomized query-processing techniqueguarantees avoiding local minima and allows the meaningful indexing of new media objects and new terms.The present adaptive search engine allows for the efficient community creation and updating of social mediaindexes, which is able to instill and propagate deep knowledge into social media concerning the advancedsearch and usage of media resources. Experiments with v arious relevance distribution settings have shownefficient convergence of such indexes, which enable intelligent search and sharing of social media resourcesthat are otherwise hard to discover.