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A "good" peer to which the user's query should be forwarded would have themat-ically relevant index contents, which could be measured by statistical notions of similarity between peers. With such a highly specialized and personalized "power search engine" most queries should be executed locally, but once in a while the user may not be satisfied with the local results and would then want to contact other peers. The crawler may be thematically focused or crawl results may be postprocessed so that the local index contents reflects the corresponding user's interest profile. The DELIS project is aiming at a P2P system where each peer has a full-fledged Web search engine, including a crawler and an index manager. Moroever, a peer-to-peer setting is ideally suited to capture lo-cal user behavior, like query logs and click streams, and disseminate and aggre-gate this information in the network, at the discretion of the corresponding user, in order to incorporate richer cognitive models. In addition, peers can dynamically collaborate on advanced and particularly difficult queries.
In a network with many thousands or millions of peers the storage and access load require-ments per peer are much lighter than for a centralized Google-like server farm thus more powerful techniques from information retrieval, statistical learning, computational linguistics, and ontological reasoning can be employed on each peer's local search engine for boosting the quality of search results. The peer-to-peer (P2P) computing paradigm is an intriguing alternative to Google-style search engines for querying and ranking Web content. We show how we can greatly benefit from these additional semantic relations during query routing in search engines, such as Minerva, and in the JXP algorithm, which computes the PageRank authority measure in a completely decentralized manner. The proposed techniques can be easily integrated into existing systems as they require only small additional bandwidth consumption as most messages can be piggybacked onto established communication.
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Peers are free to decide which connections they create and which they want to avoid based on various usefulness estimators. In contrast, our strategy follows the spirit of peer autonomy and creates semantic overlay networks based on the notion of “peer-to-peer dating”. In most approaches, peers are connected to other peers based on a rigid semantic profile by clustering them based on their contents. In recent research a rather strict notion of semantic overlay networks has been established. However, the problem inherent with this scenario is selecting a few promising peers out of an a priori unlimited number of peers. A challenging task in such systems is to efficiently route user queries to peers that can deliver high quality results and be able to rank these returned results, thus satisfying the users’ information need. We consider a network of autonomous peers forming a logically global but physically distributed search engine, where every peer has its own local collection generated by independently crawling the Web.
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Our preliminary experiments, based on real-life query-log and click-stream traces from eight different trial users indicate significant improvements in the precision of search results.
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The enhanced PageRank scores, coined QRank scores, can be computed offline at query-time they are combined with query-specific relevance measures with virtually no overhead. This approach generalizes the well-known random-surfer model into a random-expert model that mimics the behavior of an expert user in an extended session consisting of queries, query refinements, and result-navigation steps. This paper presents a new method that incorporates the notion of query nodes into PageRank model and integrates the implicite relevance feedback given by click streams into the automated process of authority analysis. Query log and click streams obtained from web browsers or search engines can contribute to better quality by exploiting the collaborative recommendations that are implicitly embedded in this information. The ongoing explosion of web information calls for more intelligent and personalied methods towards better search result quality for advanced queries.
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