Document filtering method using non-relevant information profile

Hoashi, Keiichiro; Matsumoto, Kazunori; Inoue, Nozomu; Hashimoto, Kazuo

doi:10.1145/345508.345573

Cited by 26 publications

(19 citation statements)

References 2 publications

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“…The results obtained for 3 subjects in every research field using methods (1) to (8) are presented in Table 2. The MAP values M i obtained for every subject using method i (i = 1 .…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…Thus, filtering accuracy is improved by recalculation of the similarity scores using the updated interest profile. Studies of RF include the application of the subspace classification method to Japanese newspaper search [7], use of the irrelevance profile for foreign newspaper search [8], and so on. However, the RF approach involves evaluation of filtering results, and thus imposes a burden on the user, which is contradictory to Requirement A.…”

Section: Previous Research On Document Filtering and Remaining Problemsmentioning

confidence: 99%

“…The filtering experiments were performed using methods (1) to (7), which combine the TRR, the subject category PRF, and the coauthor research group PRF, and baseline method (8). …”

Section: Evaluation Criteria and Proceduresmentioning

confidence: 99%

See 2 more Smart Citations

Paper filtering method using features of coauthor research group, subject category, and terminology

Yamamoto¹,

Mase²,

Yajima³

et al. 2010

Elect Comm in Japan

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SUMMARYA paper filtering system that supports the effective collection of related technical papers is becoming important as technological progress accelerates. Two requirements for the paper filtering system are (1) reduction of the user workload in specifying the filtering conditions and (2) sufficient filtering accuracy. We propose a paper filtering method that meets both requirements simultaneously by focusing on the features of the coauthor research group, subject category, and terminology. The result of evaluation using 3600 domestic learned-society papers shows that the proposed method improved the mean average precision from 0.39 to 0.50, that is, by 0.11, compared with the conventional pseudo-relevance feedback method, thus improving its suitability for practical use.

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“…The results obtained for 3 subjects in every research field using methods (1) to (8) are presented in Table 2. The MAP values M i obtained for every subject using method i (i = 1 .…”

Section: Evaluation Resultsmentioning

confidence: 99%

Section: Previous Research On Document Filtering and Remaining Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Paper filtering method using features of coauthor research group, subject category, and terminology

Yamamoto¹,

Mase²,

Yajima³

et al. 2010

Elect Comm in Japan

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show abstract

“…In [23], [24], [25], and [27] utilize relevance feedback to update the user profile. Although relevance feedback is effective, users are overloaded.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Improvement of Semantic Search Results with Providing an Updatable Dynamic User Model

Karimi-Mansoub¹,

Abri²

2016

IJCA

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The current generation of search engines is severely limited in its understanding of the user's intent and the Web's content and consequently in matching the needs for information with the vast supply of resources on the web [10]. The search engines are evolving from the keyword matching search in general to conception match search personalized by user. In this article, is tried to use the semantic search engines to improve the efficiency level of personalization process. Since the general problem in the search results produced by semantic search is the overload information and the mismatch between the results and corresponding requirements needed by the user, so there is an attempt to troubleshoot the problems by applying user model to some extent. The research project at hand is oriented to collect personalized data to be displayed and create the user model. Models create a structure to display the information on the basis of the user's priorities. The initial step to develop a user modeling is to collect and compile user's interests. In this paper, is studied how to infer a user's interest from the user's search context and use the inferred implicit user model for personalized search.The main focus in this research, is on the embetterment of the efficiency of the results produced by the semantic search of updated user model. Since the low efficiency of personalized processes by the user model is due to low level factors and components by which user model is built up and the algorithm by which the model is updated, so in this research is tried to improve the efficiency of user models by investigating and modifying these cases and as a result, the improvement of search results. This paper presents to research fields of user model and semantic search and also it attempts to show how recent semantic research procedures in web development can intermingle with modern technologies of user model. Some experiments were carried out to evaluate the suggested procedures for user profile and the results showed some improvement in the user's satisfaction when the user used their profiles to personalize the results from semantic search. KeywordsSemantic search; implicit user model; Ontology; personalized search; user search history.Supported by Turkey BİDEB program.

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“…Although profiles are typically built only from topics of interest to the user, some projects have explored including information about non-relevant topics in the profile [35,104]. In these approaches, the system is able to use both kinds of topics to identify relevant documents and discard non-relevant documents at the same time.…”

Section: Introductionmentioning

confidence: 99%

User Profiles for Personalized Information Access

Gauch¹,

Speretta²,

Chandramouli³

et al.

Lecture Notes in Computer Science

383

221

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Abstract. The amount of information available online is increasing exponentially. While this information is a valuable resource, its sheer volume limits its value. Many research projects and companies are exploring the use of personalized applications that manage this deluge by tailoring the information presented to individual users. These applications all need to gather, and exploit, some information about individuals in order to be effective. This area is broadly called user profiling. This chapter surveys some of the most popular techniques for collecting information about users, representing, and building user profiles. In particular, explicit information techniques are contrasted with implicitly collected user information using browser caches, proxy servers, browser agents, desktop agents, and search logs. We discuss in detail user profiles represented as weighted keywords, semantic networks, and weighted concepts. We review how each of these profiles is constructed and give examples of projects that employ each of these techniques. Finally, a brief discussion of the importance of privacy protection in profiling is presented. IntroductionIn the modern Web, as the amount of information available causes information overloading, the demand for personalized approaches for information access increases. Personalized systems address the overload problem by building, managing, and representing information customized for individual users. This customization may take the form of filtering out irrelevant information and/or identifying additional information of likely interest for the user. Research into personalization is ongoing in the fields of information retrieval, artificial intelligence, and data mining, among others.This chapter discusses user profiles specifically designed for providing personalized information access. Other types of profiles, build using different construction techniques, are described elsewhere in this book. In particular, Chapter 4 [40] dis-

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Document filtering method using non-relevant information profile

Cited by 26 publications

References 2 publications

Paper filtering method using features of coauthor research group, subject category, and terminology

Paper filtering method using features of coauthor research group, subject category, and terminology

Improvement of Semantic Search Results with Providing an Updatable Dynamic User Model

User Profiles for Personalized Information Access

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