Using thematic ontologies for user- and group- based adaptive personalization in web searching

This paper presents Prospector, an adaptive meta-search layer, which performs personalized re-ordering of search results. Prospector combines elements from two approaches to adaptive search support: (a) collaborative web searching; and, (b) personalized searching using semantic metadata. The paper focuses on the way semantic metadata and the users’ search behavior are utilized for user- and group- modeling, as well as on how these models are used to re-rank results returned for individual queries. The paper also outlines past evaluation activities related to Prospector, and discusses potential applications of the approach for the adaptive retrieval of multimedia documents

Personalized information retrieval in digital ecosystems

Search results personalization is considered a promising approach to boost the quality of text retrieval. In this paper, a personalized information retrieval paradigm is proposed which not only implicitly creates user profile by learning users? search history, search preferences, and desktop information by kNN algorithm; but also intends to deal with the problem of search concepts drift through adjusting theweight of category which represents users? search preference.By comparing the cosine similarities between vectors represent personal valued search concepts in user profiles, and vectors represent search concepts in the retrieved search results, the search results will be tailed to better match users? information needs

Determining and satisfying search users real needs via socially constructed search concept classification

The focus of the research is to disambiguate search query by categorizing search results returned by search engines and interacting with the user to achieve query and results refinement. A novel special search-browser has been developed which combines search engine results, the Open DirectoryProject (ODP) based lightweight ontology as navigator and classifier, and search results categorizing. Categories are formed based on the ODP as a predefined ontology and Lucene is to be employed to calculate the similarity between retrieved items of the search engine and concepts in the ODP. With theinteraction of users, the search-browser improves the quality of search results by excluding the irrelevant documents and ontologically categorizing results for user inspection

Linking Data Across Universities: An Integrated Video Lectures Dataset

This paper presents our work and experience interlinking educational information across universities through the use of Linked Data principles and technologies. More specifically this paper is focused on selecting, extracting, structuring and interlinking information of video lectures produced by 27 different educational institutions. For this purpose, selected information from several websites and YouTube channels have been scraped and structured according to well-known vocabularies, like FOAF 1, or the W3C Ontology for Media Resources 2. To integrate this information, the extracted videos have been categorized under a common classification space, the taxonomy defined by the Open Directory Project 3. An evaluation of this categorization process has been conducted obtaining a 98% degree of coverage and 89% degree of correctness. As a result of this process a new Linked Data dataset has been released containing more than 14,000 video lectures from 27 different institutions and categorized under a common classification scheme

Improving web search by categorization, clustering, and personalization

This research combines Web snippet1 categorization, clustering and personalization techniques to recommend relevant results to users. RIB - Recommender Intelligent Browser which categorizes Web snippets using socially constructed Web directory such as the Open Directory Project (ODP) is to bedeveloped. By comparing the similarities between the semantics of each ODP category represented by the category-documents and the Web snippets, the Web snippets are organized into a hierarchy. Meanwhile, the Web snippets are clustered to boost the quality of the categorization. Based on an automatically formed user profile which takes into consideration desktop computer informationand concept drift, the proposed search strategy recommends relevant search results to users. This research also intends to verify text categorization, clustering, and feature selection algorithms in the context where only Web snippets are available

Personalized Search Engine: A Review

Now a days there is A major problem in mobile search is that the interactions between the users and search engines are limited by the small form factors of the mobile devices. As a result, mobile users tend to submit shorter, hence, more ambiguous queries compared to their web search counterparts. In order to return highly relevant results to the users, mobile search engines must be able to profile the users� interests and personalize the search results according to the user�s profiles. A personalized mobile search engine (PMSE) that captures the users� preferences in the form of concepts by mining their click through data. Due to the importance of location information in mobile search, In this paper PMSE classifies these concepts into content concepts and location concepts To characterize the diversity of the concepts associated with a query and their relevance�s to the user�s need, four entropies are introduced to balance the weights between the content and location facets

Discovering semantic aspects of socially constructed knowledge hierarchy to boost the relevance of Web searching

The research intends to boost the relevance of Web search results by classifyingWebsnippet into socially constructed hierarchical search concepts, such as the mostcomprehensive human edited knowledge structure, the Open Directory Project (ODP). Thesemantic aspects of the search concepts (categories) in the socially constructed hierarchicalknowledge repositories are extracted from the associated textual information contributed bysocieties. The textual information is explored and analyzed to construct a category-documentset, which is subsequently employed to represent the semantics of the socially constructedsearch concepts. Simple API for XML (SAX), a component of JAXP (Java API for XMLProcessing) is utilized to read in and analyze the two RDF format ODP data files, structure.rdfand content.rdf. kNN, which is trained by the constructed category-document set, is used tocategorized the Web search results. The categorized Web search results are then ontologicallyfiltered based on the interactions of Web information seekers. Initial experimental resultsdemonstrate that the proposed approach can improve precision by 23.5%

WebTailor: Internet Service for Salient and Automatic User Interest Profiles

Website personalization systems seek to give users unique, tailored content and experiences on the Internet. A key feature of these systems is a user profile that represents each user in a way that distinguishes them from others. In current personalization systems, the data used to create these profiles is extremely limited, which leads to a host of problems and ineffectual personalization. The main goal of this thesis is to improve these personalization systems by addressing their lack of data and its poor quality, breadth, and depth. This is accomplished by analyzing and classifying the content of each user\u27s Internet browsing activity, rather than just their activity on a single website, to autonomously build persistent, ontology-based user profiles. Furthermore, these profiles are built and stored on a remote server, which allows them to be easily made available to approved websites in the interest of providing the data to enable accurate, relevant, and up-to-date personalization