The evaluation of a social adaptive website for cultural events

Gena, Cristina; Cena, Federica; Vernero, Fabiana; Grillo, Pierluigi

doi:10.1007/s11257-012-9129-9

Cited by 16 publications

(11 citation statements)

References 42 publications

(43 reference statements)

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“…() identify the roles of ‘lurkers’ and ‘posters’, where lurkers are members of online communities who read, but do not post, and posters are the few members who post content. These results have been recently confirmed by Gena et al . (nd): the results show that the most participating users contribute in the form of small contributions (clicking on a tag for insertion and clicking on like/dislike) and just a few of them generate bottom‐up contents. Analysing the users' tagging activity, they reported that 84% of user tags were the ones proposed by the system and just clicked on by the users, whereas the remaining 16% were inserted by users as free text.…”

Section: Related Worksupporting

confidence: 54%

Leveraging social semantic components in executable environments for learning

2013

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Learning can benefit from the modern web structure through the convergence of top-down encyclopedic institutional knowledge and bottom-up user-generated annotations. A promising approach to such convergence consists in leveraging the social functionalities in 3.0 executable environments through the recommendation of tags with the mediation of lexical and semantic resources.This paper addresses such issues through the design and evaluation of a tag recommendation system in a Web 3.0 web portal, "150 Digit". Designed for schools, "150 Digit" encourages students and teachers to interact with a set of four exhibitions on the historical and social aspects of the Italian unification process in a virtual environment. The web site displays the exhibits and their related documents promoting the users' active participation through tagging, voting, and commenting the exhibits. Tags become a way for students to create and explore new relations among the site contents, orthogonal to the institutional viewpoint. In this paper, we illustrate the recommendation strategy incorporated in "150 Digit", which relies on a semantic middleware to mediate between the input expressed by the users through tags and the top-down institutional classification provided by the curators of the exhibitions. Following on, we describe the evaluation process conducted in a real experimental setting, and discuss the evaluation results and their implications for learning environments.

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Section: Related Worksupporting

confidence: 54%

Leveraging social semantic components in executable environments for learning

2013

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“…To answer [RQ3], we compared the DSR algorithm with the iCITY algorithm, that had been given a positive performance rating in a real-world evaluation that lasted a few months (Gena et al, 2013). We compared them adopting precision, recall, accuracy and F 1 , that are well-known metrics for recommender systems (Shani and Gunawardana, 2011).…”

Section: Resultsmentioning

confidence: 99%

“… to evaluate the usefulness of DSRs generated by our "social" DSR algorithm and to compare them with other, more traditional, kinds of recommendations;  to understand whether the item-recommendation part of our DSR algorithm has comparable performance, in terms of standard measures, such as precision and recall, to a collaborative filtering algorithm, in the context of a large offline study;  to understand whether, in the context of an online user study, our DSR algorithm performs better than a traditional content-based one which makes use of detailed information on the target user's preferences proved to have a good performance with a consolidated user model. The algorithm is described in detail by Carmagnola et al, 2008 andGena et al, 2013 for information on its evaluation;  to assess how much importance users give to the recommended item and group, respectively, when they are evaluating a DSR in a particular domain (we surmised that user preferences might depend on the type of suggestion they receive). This information is useful to understand how these two elements should contribute to the overall predicted score for a recommendation.…”

Section: Introductionmentioning

confidence: 99%

What and who with: A social approach to double-sided recommendation

Lombardi

Vernero

2017

International Journal of Human-Computer Studies

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Double-sided recommendations (DSR) have been recently introduced for an item and a group that the item is destined for. Herein we present an algorithm which takes inspiration from The Social Comparison Theory to recommend items that had an average positive evaluation from other users on the target user's social network. Other users' judgments are weighted according to the influence these users have on the target. Moreover, for each recommended item, we propose a group that encompasses all the target users' contacts who expressed a positive opinion on it. Our data show that users consider double-sided recommendations more useful than traditional recommendations which provide equivalent information. It was observed that our "social" DSR algorithm performs better in the event recommendation domain than a content-based one which has already been recognised as providing a good performance, in terms of precision, recall, accuracy and F1.This result is strengthened by our demonstrating that the good performance DSRs provide also depends on their peculiar structure and not only on the fact that they include "social" information. The itemrecommendation part also performed better than a user-based collaborative filtering algorithm. Lastly, we found that users' scores for recommended item-group packages can be better predicted by considering only the system scores for the recommended groups, at least in the domain of social and cultural events.

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“…The evaluation of a complex system, such as a semantic adaptive social system, requires the integration of methodologies from different areas (adaptive systems, recommender systems, social sciences) and the use of both qualitative and quantitative approaches. In particular, it is necessary to implement a combination of evaluation methodologies in order to verify all of the aspects involved in a semantic social application; mere qualitative evaluation is not enough (see Gena et al [2013]). In this sense, the article by Biancalana et al is a good example of an exhaustive evaluation, albeit does not present a novel approach.…”

Section: Discussionmentioning

confidence: 99%

Perspectives in semantic adaptive social web

Cena

Dattolo

Lops

et al. 2013

ACM Trans. Intell. Syst. Technol.

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The Social Web is now a successful reality with its quickly growing number of users and applications. Also the Semantic Web, which started with the objective of describing Web resources in a machine-processable way, is now outgrowing the research labs and is being massively exploited in many websites, incorporating high-quality user-generated content and semantic annotations. The primary goal of this special section is to showcase some recent research at the intersection of the Social Web and the Semantic Web that explores the benefits that adaptation and personalization have to offer in the Web of the future, the so-called Social Adaptive Semantic Web. We have selected two articles out of fourteen submissions based on the quality of the articles and we present the main lessons learned from the overall analysis of these submissions.

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The evaluation of a social adaptive website for cultural events

Cited by 16 publications

References 42 publications

Leveraging social semantic components in executable environments for learning

Leveraging social semantic components in executable environments for learning

What and who with: A social approach to double-sided recommendation

Perspectives in semantic adaptive social web

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