Trust-aware information retrieval in peer-to-peer environments

Information Retrieval in P2P environments (P2PIR) has become an active field of research due to the observation that P2P architectures have the potential to become as appealing as traditional centralised architectures. P2P networks are formed with voluntary peers that exchange information and accomplish various tasks. Some of them may be malicious peers spreading untrustworthy resources. However, existing P2PIR systems only focus on finding relevant documents, while trustworthiness of documents and document providers has been ignored. Without prior experience and knowledge about the network, users run the risk to review,download and use untrustworthy documents, even if these documents are relevant. The work presented in this dissertation provide the first integrated framework for trust-aware Information Retrieval in P2P environments, which can retrieve not only relevant but also trustworthy documents. The proposed content trust models extend an existing P2P trust management system, PeerTrust, in the context of P2PIR to compute the trust values of documents and document providers for given queries. A method is proposed to estimate global term statistics which are integrated with existing relevance-based approaches for document ranking and peer selection. Different approaches are explored to find optimal parametersettings in the proposed trust-aware P2PIR systems. Moreover, system architectures and data management protocols are designed to implement the proposed trust-aware P2PIR systems in structured P2P networks. The experimental evaluation demonstrates that P2PIR can benefit from trust-aware P2PIR systems significantly. It can importantly reduce the possibility of untrustworthy documents in the top-ranked result list. The proposed estimated global term statistics can provide acceptable and competitive retrieval accuracy within different P2PIR scenarios.EThOS - Electronic Theses Online ServiceORSSchool ScholarshipGBUnited Kingdo

Query routing in cooperative semi-structured peer-to-peer information retrieval networks

Conventional web search engines are centralised in that a single entity crawls and indexes the documents selected for future retrieval, and the relevance models used to determine which documents are relevant to a given user query. As a result, these search engines suffer from several technical drawbacks such as handling scale, timeliness and reliability, in addition to ethical concerns such as commercial manipulation and information censorship. Alleviating the need to rely entirely on a single entity, Peer-to-Peer (P2P) Information Retrieval (IR) has been proposed as a solution, as it distributes the functional components of a web search engine – from crawling and indexing documents, to query processing – across the network of users (or, peers) who use the search engine. This strategy for constructing an IR system poses several efficiency and effectiveness challenges which have been identified in past work. Accordingly, this thesis makes several contributions towards advancing the state of the art in P2P-IR effectiveness by improving the query processing and relevance scoring aspects of a P2P web search. Federated search systems are a form of distributed information retrieval model that route the user’s information need, formulated as a query, to distributed resources and merge the retrieved result lists into a final list. P2P-IR networks are one form of federated search in routing queries and merging result among participating peers. The query is propagated through disseminated nodes to hit the peers that are most likely to contain relevant documents, then the retrieved result lists are merged at different points along the path from the relevant peers to the query initializer (or namely, customer). However, query routing in P2P-IR networks is considered as one of the major challenges and critical part in P2P-IR networks; as the relevant peers might be lost in low-quality peer selection while executing the query routing, and inevitably lead to less effective retrieval results. This motivates this thesis to study and propose query routing techniques to improve retrieval quality in such networks. Cluster-based semi-structured P2P-IR networks exploit the cluster hypothesis to organise the peers into similar semantic clusters where each such semantic cluster is managed by super-peers. In this thesis, I construct three semi-structured P2P-IR models and examine their retrieval effectiveness. I also leverage the cluster centroids at the super-peer level as content representations gathered from cooperative peers to propose a query routing approach called Inverted PeerCluster Index (IPI) that simulates the conventional inverted index of the centralised corpus to organise the statistics of peers’ terms. The results show a competitive retrieval quality in comparison to baseline approaches. Furthermore, I study the applicability of using the conventional Information Retrieval models as peer selection approaches where each peer can be considered as a big document of documents. The experimental evaluation shows comparative and significant results and explains that document retrieval methods are very effective for peer selection that brings back the analogy between documents and peers. Additionally, Learning to Rank (LtR) algorithms are exploited to build a learned classifier for peer ranking at the super-peer level. The experiments show significant results with state-of-the-art resource selection methods and competitive results to corresponding classification-based approaches. Finally, I propose reputation-based query routing approaches that exploit the idea of providing feedback on a specific item in the social community networks and manage it for future decision-making. The system monitors users’ behaviours when they click or download documents from the final ranked list as implicit feedback and mines the given information to build a reputation-based data structure. The data structure is used to score peers and then rank them for query routing. I conduct a set of experiments to cover various scenarios including noisy feedback information (i.e, providing positive feedback on non-relevant documents) to examine the robustness of reputation-based approaches. The empirical evaluation shows significant results in almost all measurement metrics with approximate improvement more than 56% compared to baseline approaches. Thus, based on the results, if one were to choose one technique, reputation-based approaches are clearly the natural choices which also can be deployed on any P2P network