Peer to Peer Information Retrieval: An Overview

Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom

Using Search Engine Technology to Improve Library Catalogs

This chapter outlines how search engine technology can be used in online public access library catalogs (OPACs) to help improve users’ experiences, to identify users’ intentions, and to indicate how it can be applied in the library context, along with how sophisticated ranking criteria can be applied to the online library catalog. A review of the literature and current OPAC developments form the basis of recommendations on how to improve OPACs. Findings were that the major shortcomings of current OPACs are that they are not sufficiently user-centered and that their results presentations lack sophistication. Further, these shortcomings are not addressed in current 2.0 developments. It is argued that OPAC development should be made search-centered before additional features are applied. While the recommendations on ranking functionality and the use of user intentions are only conceptual and not yet applied to a library catalogue, practitioners will find recommendations for developing better OPACs in this chapter. In short, readers will find a systematic view on how the search engines’ strengths can be applied to improving libraries’ online catalogs

A New Approach of Clustering Feedback Sessions for Inferring User Search Goals

Internet information is growing every day exponentially. In order to find out the exact required information from this web search engines has become absolutely necessary tool for the web users. It has also become more difficult to provide user the required information. When Different users provide an ambiguous query to a search engine, they might be having different search goals. Therefore, it is required to find and analyze user search goals to improve the performance of a search engine and user experience. By representing the results in cluster we find out different user search goals for a query. It has advantages in improving search engine relevance and user experience. It extends the delivery and quality of internet information services to the end user. It also improves performance of Web server system. Query classification, search result reorganization and session boundary detection are the approaches attempt to find out user search goals. But the mentioned approaches has many limitations. A new approach has been implemented that overcomes the limitations and analyze, discover user search goals using feedback sessions. This approach first takes the user search query. For each single result of the search query pseudo-documents are generated. Using K-means++ clustering algorithm, these pseudo-documents are clustered. Each cluster can be considered as one user search goal. Finally in restructured result is given to the user where each URL is categorized into a cluster centered by the inferred search goals. Then depending upon user click through, results are restructured and represented to the user in order to satisfy the information need. DOI: 10.17762/ijritcc2321-8169.15071

Porqpine: a peer-to-peer search engine

In this paper, we present a fully distributed and collaborative search engine for web pages: Porqpine. This system uses a novel query-based model and collaborative filtering techniques in order to obtain user-customized results. All knowledge about users and profiles is stored in each user node?s application. Overall the system is a multi-agent system that runs on the computers of the user community. The nodes interact in a peer-to-peer fashion in order to create a real distributed search engine where information is completely distributed among all the nodes in the network. Moreover, the system preserves the privacy of user queries and results by maintaining the anonymity of the queries? consumers and results? producers. The knowledge required by the system to work is implicitly caught through the monitoring of users actions, not only within the system?s interface but also within one of the most popular web browsers. Thus, users are not required to explicitly feed knowledge about their interests into the system since this process is done automatically. In this manner, users obtain the benefits of a personalized search engine just by installing the application on their computer. Porqpine does not intend to shun completely conventional centralized search engines but to complement them by issuing more accurate and personalized results.Postprint (published version