NewsMe: A Case Study for Adaptive News Systems with Open User Model

Wongchokprasitti, Chirayu; Brusilovsky, Peter

doi:10.1109/conielecomp.2007.88

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…For example in [1], the authors make use of a machine learning classification framework to filter news following the user's choices. Another technique for adaptive news, which is based on user modeling, is presented in [19], where the system maintains separate user models for each topic of news. However these systems lack serving of news summaries and use fairly trivial keyword extraction techniques.…”

Section: Related Workmentioning

confidence: 99%

“…We are thus researching the usage of multiple data mining and retrieval techniques which are incorporated to the proposed system. This is exactly the key feature that distinguishes the to-be presented approach from similar ones like [19,8,14]. The contribution of the current article is twofold: the evaluation of the effect that part of speech (POS) tagging has on text summarization, and the presentation of the applied personalization algorithm with regard to the content delivery to the user's desktop.…”

Section: Introductionmentioning

confidence: 99%

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Noun retrieval effect on text summarization and delivery of personalized news articles to the user’s desktop

Bouras¹,

Tsogkas²

2010

Data & Knowledge Engineering

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noun retrieval effect on text summarization and delivery of personalized news articles to the user’s desktop

Bouras¹,

Tsogkas²

2010

Data & Knowledge Engineering

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“…NewsMe [33] is an adaptive recommendation system based on an open user model, which monitors 81 RSS channels from 21 sources. The core of recommendation method is based on the Nearest Neighbor algorithm.…”

Section: Related Workmentioning

confidence: 99%

Effective hierarchical vector-based news representation for personalized recommendation

2012

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With amount of information on the web, users often require functionality able to filter the content according to their preferences. To solve the problem of overwhelmed users we propose a content-based recommender. Our method for the personalized recommendation is dedicated to the domain of news on the Web. We propose an effective representation of news and a user model which are used to recommend dynamically changing large number of text documents. We work with the vector representation of the news and hierarchical representation of similarities among items. Our representation is designed with aim to effectively estimate user needs and generate personalized list of items in information space. This approach is unique thanks its low complexity and ability to work in real-time with no visible delay for the user. To evaluate our approach we experimented with real information space of largest Slovak newspaper and simulated recommending.

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“…Furthermore, there has been mounting interest in organizing news and newspaper articles using automatic techniques such as text classification and indexing algorithms (e.g. Chen and Lin 2000;Evans and Klavans 2003;Casillas et al 2003;Mamakis et al 2011;Rocha and Cobo 2011), or, on occasion, those based on user-features, in a usercentered fashion, such as the automatic summarization and categorization of news derived from user choices (Banos et al 2006), user modeling (Wongchokprasitti and Brusilovsky 2007), or user profiles (Bouras and Tsogkas 2010). However, as some authors have pointed out, automatic indexing and user-based retrieval systems such as Google's are not exempt from bias or subjectivity either (e.g.…”

Section: Media Knowledge Organization In the Context Of Ko Studiesmentioning

confidence: 99%

Critical Organization of Knowledge in Mass Media Information Systems

García-Gutiérrez¹,

Martínez-Ávila²

2014

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Antonio García Gutiérrez holds a Ph.D. in information sciences from Complutense University of Madrid and is a professor at the School of Communication in Seville University. He has acted as an information systems consultant for international organizations such as UNESCO, World Tourism Organization and as an evaluator of scientific projects for the Fifth Framework Programme of the European Union. He has written more than fifteen books that address the problems of knowledge organization and its construction from post-epistemological stances linked to objects related to cultural studies and the postcolonial approach, such as the memory, the experience, the identity and the culture. Abstract: This paper studies knowledge organization (KO) in media archives, focusing on the presence of subjectivity in the core tasks of mass media knowledge organizers (MKOS) dealing with press, radio and TV records, such as classification, representation, and any other process related to content analysis and organization in news information systems. Far from rejecting subjectivity and ideological bias in these operations -since they coparticipate in the media construction of reality-the authors consider MKOS to be genuine ideological and cultural mediators with the right and social responsibility to explicitly state the results of their "objectifiable" work (obtained through KO protocols and procedures determined by the media/company, classifications, thesauri, ontologies, etc.) and differentiate them from those of their political, ideological, cultural and, in sum, subjective stances. In order to achieve this, we propose the application of critical operators that should be followed by technical, collaborative and even technological actions geared to investing information systems with the capacity to consider those stances and allowing users to distinguish them. In short, it is the theoretical recognition of the subjective and biased presence of media knowledge organization operators in a job that is usually considered neutral, banal and even objective, and the initial development of tools for critical, self-critical, technical, and technological training keyed to its practical solution. This paper outlines the lines of work of a broader research study on the critical function of KO in the field of global media memory.

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NewsMe: A Case Study for Adaptive News Systems with Open User Model

Cited by 8 publications

References 10 publications

Noun retrieval effect on text summarization and delivery of personalized news articles to the user’s desktop

Noun retrieval effect on text summarization and delivery of personalized news articles to the user’s desktop

Effective hierarchical vector-based news representation for personalized recommendation

Critical Organization of Knowledge in Mass Media Information Systems

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