Selection and Ranking of Activities in the Social Web

Lombardi, Ilaria; Likavec, Silvia; Picardi, Claudia; Chiabrando, Elisa

doi:10.1007/978-3-319-03524-6_32

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…This study has provided the basis for a prototype content-based recommender system described in Lombardi et al (2013), where the user can browse a list of events sorted by the prediction of a scoring function similar to the one we described, whose coefficients can however be manually tuned or completely switched off depending on the user's intention and context. In this implementation, additional factors are further refined.…”

Section: Discussionmentioning

confidence: 99%

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Should I Stay or Should I Go? Improving Event Recommendation in the Social Web

et al. 2014

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This paper focuses on the recommendation of events in the Social Web, and addresses the problem of finding if, and to which extent, certain features, which are peculiar to events, are relevant in predicting the users' interests and should thereby be taken into account in recommendation.We consider in particular three "additional" features that are usually shown to users within social networking environments: reachability from the user location, the reputation of the event in the community, and the participation of the user's friends. Our study is aimed at evaluating whether adding this information to the description of the event type and topic, and including in the user profile the information on the relevance of these factors, can improve our capability to predict the user's interest.We approached the problem by carrying out two surveys with users, who were asked to express their interest in a number of events. We then trained, by means of linear regression, a scoring function defined as a linear combination of the different factors, whose goal was to predict the user scores. We repeated this experiment under different hypotheses on the additional factors, in order to assess their relevance by comparing the predictive capabilities of the resulting functions.The compared results of our experiments show that additional factors, if properly weighted, can improve the prediction accuracy with an error reduction of 4.1%. The best results were obtained by combining content-based factors and additional factors in a proportion of approximately 10 : 4.

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Section: Discussionmentioning

confidence: 99%

“…Moreover, it can provide data for evaluating refined versions of the additional factors introduced here. The prototype described by Lombardi et al (2013) is a first step in this direction.…”

Section: Discussionmentioning

confidence: 99%