A semantic-based approach for searching and browsing tag spaces

Vandic, Damir; Dam, Jan-Willem van; Frăsincar, Flavius

doi:10.1016/j.dss.2012.08.010

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Second, the participants were invited to identify a group of tags concerning "energy conservation." As the users identified a tag cluster, they might have been more experienced in tag locating, and thus, in tag-based information locating (Vandic et al, 2012); therefore, the order of the search experiment and supplementary test, as well as the two sub-tests in the supplementary experiment, could not be swapped. Finally, the post-questionnaire was designed to elucidate the details of search behavior when graphic-based TCs were applied to information searches, the preferences for TCs, and testees' search experiences.…”

Section: Comparative Study Of Graphic-based Tag Cloudsmentioning

confidence: 99%

Comparative study of graphic-based tag clouds: theory and experimental evaluation for information search

2020

OIR

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PurposeGraphic-based tag clouds aim to visually represent tag content and tag structure, and then to better represent tagged information for later search. However, few studies have clarified the features among varied visualization approaches involved in graphic-based tag clouds and compared them for the purpose of information search.Design/methodology/approachAfter reviewing four kinds of graphic-based tag clouds, an experimental demonstration was conducted in our study to verify how user performs in information search for a general seeking task by using them. Precision ratio, recall ratio, clicks on search and time for search were four variables tested in the experiment. Also, two supplementary tests were respectively carried out to manifest how graphic-based tag clouds contributed to the identification of target tags and tag clusters.FindingsThe experimental results showed that compared to tag content visual tag structure was more important to find related tags from tag clouds for information search. In addition, tag clouds that visually represented the semantic relationships within tags could make user more confident about their search result and carry out a shorter learning process during searching, which signified a tag-based information search path when visual elements were applied.Originality/valueThis research is one of the first to illustrate systematically the graphic-based tag clouds and their impacts on information search. The research findings could suggest on how to build up more effective and interactive tag clouds and make proposition for the design of search user interface by using graphic-based tag clouds.

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Section: Comparative Study Of Graphic-based Tag Cloudsmentioning

confidence: 99%

Comparative study of graphic-based tag clouds: theory and experimental evaluation for information search

2020

OIR

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“…To describe our proposal in detail, we use the following Formula (11) to (13) to express the three changes.…”

Section: Fig 5 Three Kinds Of Modified Changes Between Two Sessionsmentioning

confidence: 99%

“…However, it is widely accepted that the collaborative filtering approach suffers from sparsity and cold start problems, the recent work can not well solve this problem in the OLAP session recommendation, which will affect the final effectiveness of recommendation. Morever, because of the speciality in OLAP field, the recommended OLAP session without a clear insight is also an obstruction to the newcomer [11][12].…”

Section: Introductionmentioning

confidence: 99%

OLAP4R: A Top-K Recommendation System for OLAP Sessions

2017

KSII TIIS

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The Top-K query is currently played a key role in a wide range of road network, decision making and quantitative financial research. In this paper, a Top-K recommendation algorithm is proposed to solve the cold-start problem and a tag generating method is put forward to enhance the semantic understanding of the OLAP session. In addition, a recommendation system for OLAP sessions called "OLAP4R" is designed using collaborative filtering technique aiming at guiding the user to find the ultimate goals by interactive queries. OLAP4R utilizes a mixed system architecture consisting of multiple functional modules, which have a high extension capability to support additional functions. This system structure allows the user to configure multi-dimensional hierarchies and desirable measures to analyze the specific requirement and gives recommendations with forthright responses. Experimental results show that our method has raised 20% recall of the recommendations comparing the traditional collaborative filtering and a visualization tag of the recommended sessions will be provided with modified changes for the user to understand.

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“…Typically, (multi-type) clustering is based on both the attribute values of the objects (possibly of different types) and the links among them (e.g., spectral clustering [26], LinkClus [49] and CrossClus [50]). Clustering has been also exploited as a preliminary phase for other data mining tasks, such as link prediction [3] and semantic tagging [46], as well as for the construction of higher-level features or multiple views of the data [8]. Further interesting examples can be found in studies that proposed ranking-based clustering approaches (e.g., RankClus [44] and NetClus [45]), that generate efficiently results for both ranking and clustering.…”

Section: Introductionmentioning

confidence: 99%