Onto-search: An ontology based personalized mobile search engine

Raghunathan, Divya; Robin, C. R. Rene

doi:10.1109/icgccee.2014.6921422

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…In [26], the authors propose a multidimensional profile for a mobile user, which includes the location and time in addition to users cognitive context, a case-based reasoning "CBR" approach is adopted to select the appropriate profile and reorder search results. In [27], the authors propose a personalized system that alternates between two states moving or stationary according to the users activity, each state has kinds of content information and special interface corresponds to the time, location, and a set of personal information filled before.In [11], the authors use available information on social networks to set preferences and user interest, as in [26] the authors in [9] propose a concepts representation of the interests and preferences of the user based on an ontology, and guided by mining search results and their click through as validation method, SVM is then used to re-rank future search results.…”

Section: B) Retrieval Information In Mobile Environmentmentioning

confidence: 99%

Contextual Source Selection for Federated Search in Mobile Environment

Benachour¹,

Kechid²

2016

2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA)

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The emergence of new communication technologies and the latest generation of smart-phone devices, allow the user to continuously access the Web, at any time and from any location with different devices. The introduction of the user and his environment in the research process is intended to solve the information overload problem and to increase the accuracy of the retrieval system. In this paper, we present a new approach to select the best sources from different heterogeneous sources by exploiting a multidimensional contextual user profile in a mobile environment, that includes the research situation "time, location ...", the device and the user preferences. We also present our approach to define user and source profiles, using external ontologies and a learning algorithm supervised by user results satisfaction score.

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Section: B) Retrieval Information In Mobile Environmentmentioning

confidence: 99%

Contextual Source Selection for Federated Search in Mobile Environment

Benachour¹,

Kechid²

2016

2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA)

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“…This way allows users to browse their past searches while saving each search record and its matching search results. This way includes two types of data analysis: non-automated and automated [33], [34]. Non-automated analysis needs the user to enter the relevant information to set up the browsing history.…”

Section: A Personal Searchmentioning

confidence: 99%

A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

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In this paper, we propose a method for optimizing search engine results based on user interaction. This method generates different search results mainly through the user's operation on different search topics. There are two main differences between our method and the traditional personalized search method: personal privacy and storage space. First, traditional personal search methods need to record search and click records for individual users. However, these records have great personal privacy issues for users; especially in recent years, there have been personal privacy leaks that occurred in many large online companies (such as Facebook and Yahoo). Secondly, because different users need to record their own search records, the size of the storage space is closely related to the number of users and the amount of search records stored. However, the sum of individual users' search and click records is a huge storage space in today's Internet environment. To avoid these two issues for traditional personalized search methods, this study proposes a storage-free approach based on individual users operating on different search topics. In general, in addition to avoiding personal privacy and storage space issues, our method can also achieve optimal linear time in generating personalized search results. INDEX TERMS Document clustering, machine learning, natural language processing, personalized topic search, user log.

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Interactive Topic Search System Based on Topic Cluster Technology

Chen

2020

Inf Syst Front

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Onto-search: An ontology based personalized mobile search engine

Cited by 6 publications

References 11 publications

Contextual Source Selection for Federated Search in Mobile Environment

Contextual Source Selection for Federated Search in Mobile Environment

A Study of Optimizing Search Engine Results Through User Interaction

Interactive Topic Search System Based on Topic Cluster Technology

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