Adaptive systems in the era of the semantic and social web, a survey

Torre, Ilaria

doi:10.1007/s11257-009-9067-3

Cited by 41 publications

(16 citation statements)

References 90 publications

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“…2, there are feature-based, knowledge-based, behavior-based, citation-based, contextbased, ruse-based, and many more recommendation classes [133,187,300,[303][304][305][306]. We consider the following seven classes to be most appropriate for distinguishing the approaches in the field of research-paper recommender systems:…”

Section: Survey Of the Recommendation Classesmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

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Section: Survey Of the Recommendation Classesmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…Adaptive Information Visualization has gained emerging interest in the research community, due to the increasing amount of data and the more user-centered design approaches in combination with machine learning methods [3,9,19,[26][27][28]. This emerging area aims at providing visual interfaces that adapt automatically to users' demands in terms of reducing complexity and task completion times, or providing a better user experience to increase the acceptance of visualizations.…”

Section: Introductionmentioning

confidence: 99%

Measuring context relevance for adaptive semantics visualizations

Nazemi

Kuijper

Hutter

et al. 2014

Proceedings of the 14th International Conference on Knowledge Technologies and Data-Driven Business

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Semantics visualizations enable the acquisition of information to amplify the acquisition of knowledge. The dramatic increase of semantics in form of Linked Data and Linked- Open Data yield search databases that allow to visualize the entire context of search results. The visualization of this semantic context enables one to gather more information at once, but the complex structures may as well confuse and frustrate users. To overcome the problems, adaptive visualizations already provide some useful methods to adapt the visualization on users' demands and skills. Although these methods are very promising, these systems do not investigate the relevance of semantic neighboring entities that commonly build most information value. We introduce two new measurements for the relevance of neighboring entities: The Inverse Instance Frequency allows weighting the relevance of semantic concepts based on the number of their instances. The Direct Relation Frequency inverse Relations Frequency measures the relevance of neighboring instances by the type of semantic relations. Both measurements pro- vide a weighting of neighboring entities of a selected semantic instance, and enable an adaptation of retinal variables for the visualized graph. The algorithms can easily be integrated into adaptive visualizations and enhance them with the relevance measurement of neighboring semantic entities. We give a detailed description of the algorithms to enable a replication for the adaptive and semantics visualization community. With our method, one can now easily derive the relevance of neighboring semantic entities of selected in-stances, and thus gain more information at once, without confusing and frustrating users

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“…One possible solution is, therefore, to assign semantic information to Web resources in order to facilitate their retrieval and sharing. The techniques for doing this can be divided into strong semantic techniques and weak semantic techniques [Torre 2009]. The former rely on the principles of the Semantic Web [Berners- Lee 1998], which provides a common structure to allow data to be shared and reused across applications, using ontologies (i.e., formal descriptions of concepts and their provides the contextualization and categorization of the information by analyzing and extracting the semantic domain of the user interests.…”

Section: Introductionmentioning

confidence: 99%

Social semantic query expansion

Biancalana

Gasparetti

Micarelli

et al. 2013

ACM Trans. Intell. Syst. Technol.

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Weak semantic techniques rely on the integration of Semantic Web techniques with social annotations and aim to embrace the strengths of both. In this article, we propose a novel weak semantic technique for query expansion. Traditional query expansion techniques are based on the computation of two-dimensional co-occurrence matrices. Our approach proposes the use of three-dimensional matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services, such as delicious and StumbleUpon. The results of an indepth experimental evaluation performed on both artificial datasets and real users show that our approach outperforms traditional techniques, such as relevance feedback and personalized PageRank, so confirming the validity and usefulness of the categorization of the user needs and preferences in semantic classes. We also present the results of a questionnaire aimed to know the users opinion regarding the system. As one drawback of several query expansion techniques is their high computational costs, we also provide a complexity analysis of our system, in order to show its capability of operating in real time.

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Adaptive systems in the era of the semantic and social web, a survey

Cited by 41 publications

References 90 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

Measuring context relevance for adaptive semantics visualizations

Social semantic query expansion

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