Improving Semantic Search Via Integrated Personalized Faceted and Visual Graph Navigation

Tvarožek, Michal; Barla, Michal; Frivolt, György; Tomša, Marek; Bieliková, Mária

doi:10.1007/978-3-540-77566-9_67

Cited by 11 publications

(11 citation statements)

References 15 publications

(15 reference statements)

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“…It is suitable mostly for tools realizing web search and navigation [9,12,13]. In [1], we proposed a method for initializing user model of a new user by leveraging his or her social connections to other users.…”

Section: Discussionmentioning

confidence: 99%

An Extensible Open-Source Framework for Social Network Analysis

Barla

Bieliková

2010

Advances in Intelligent and Soft Computing

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Online communities that form social networks became extremely important in many tasks related with information processing, presentation, navigation, especially in context of web-based information systems. Web-based information systems employing communities could benefit from the classical studies of human social interactions -social network analysis. In this paper, we present an extensible open-source JAVA-based framework for social network analysis which can be used either a a stand-alone application with its own GUI as well as a library within third-party software projects developed in JAVA. We provide not only a standalone desktop application, but the whole framework, which allows anyone to incorporate results of social networks analysis into their own project, which would possibly boost up its functionality, enhance the results etc.

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Section: Discussionmentioning

confidence: 99%

An Extensible Open-Source Framework for Social Network Analysis

Barla

Bieliková

2010

Advances in Intelligent and Soft Computing

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“…Also, it has been criticized that faceted search systems often only allow for conjunctive queries and consider all facets equally important for the user [26,28,30,32]. Furthermore, most approaches perform an exact matching to determine the result set.…”

Section: Interactive Recommendation and Information Filtering Approachesmentioning

confidence: 99%

“…Still, as facets and facet values are automatically inferred relying on user profiles (e.g. extracted from semantically enriched Twitter tweets [7] or social network data [30]) and observed user behavior, the user's influence on the current filter setting is limited. In addition, from a user's perspective, items may not be sufficiently described by content-related attributes [14].…”

Section: Interactive Recommendation and Information Filtering Approachesmentioning

confidence: 99%

“…To deal with lacking metadata, the system utilizes information about the file hierarchy. Other approaches use, for instance, semantic [7] or social [30] data sources for that purpose. Most of these newer works also focus on automatic extraction and adaptive selection of facets and facet values [7,18,30].…”

Section: Interactive Recommendation and Information Filtering Approachesmentioning

confidence: 99%

“…Other approaches use, for instance, semantic [7] or social [30] data sources for that purpose. Most of these newer works also focus on automatic extraction and adaptive selection of facets and facet values [7,18,30]. Still, as facets and facet values are automatically inferred relying on user profiles (e.g.…”

Section: Interactive Recommendation and Information Filtering Approachesmentioning

confidence: 99%

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Blended Recommending

Loepp

Herrmanny

Ziegler

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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We present a novel approach that integrates algorithmic recommender techniques with interactive faceted filtering methods. We refer to this approach as blended recommending. It allows users to interact with a set of filter facets representing criteria that can serve as input for different recommendation methods including both collaborative and content-based filtering. Users can select filter criteria from these facets and weight them to express their preferences and to exert control over the hybrid recommendation process. In contrast to hard Boolean filtering, the method aggregates the weighted criteria and calculates a ranked list of recommendations that is visualized and immediately updated when users change the filter settings. Based on this approach, we implemented an interactive movie recommender, MyMovieMixer. In a user study, we compared the system with a conventional faceted filtering system that served as a baseline to obtain insights into user interaction behavior and to assess recommendation quality for our system. The results indicate, among other findings, a higher level of perceived user control, more detailed preference settings, and better suitability when the search goal is vague.

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