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Kusmierczyk Tomasz, Trattner Christoph, Nørvåg Kjetil

Understanding and Predicting Online Food Recipe Production Patterns

HT '16 Proceedings of the 27th ACM Conference on Hypertext and Social Media, ACM, Halifax, NS, Canada, 2016

Studying online food patterns has recently become an active fieldof research. While there are a growing body of studies that investi-gate how online food in consumed, little effort has been devoted yetto understand how online food recipes are being created. To con-tribute to this lack of knowledge in the area, we present in this paperthe results of a large-scale study that aims at understanding howhistorical, social and temporal factors impact on the online foodcreation process. Several experiments reveal the extent to whichvarious factors are useful in predicting future recipe production.

Larrain Santiago, Parra Denis, Graells-Garrido Eduardo, Nørvåg Kjetil, Trattner Christoph

Good Times Bad Times: A Study on Recency Effects in Collaborative Filtering for Social Tagging

Proceedings of the 9th {ACM} Conference on Recommender Systems, ACM, 2015

In this paper, we present work-in-progress of a recently startedproject that aims at studying the effect of time in recommendersystems in the context of social tagging. Despite the existence ofprevious work in this area, no research has yet made an extensiveevaluation and comparison of time-aware recommendation methods.With this motivation, this paper presents results of a studywhere we focused on understanding (i) “when” to use the temporalinformation into traditional collaborative filtering (CF) algorithms,and (ii) “how” to weight the similarity between users and itemsby exploring the effect of different time-decay functions. As theresults of our extensive evaluation conducted over five social taggingsystems (Delicious, BibSonomy, CiteULike, MovieLens, suggest, the step (when) in which time is incorporated inthe CF algorithm has substantial effect on accuracy, and the typeof time-decay function (how) plays a role on accuracy and coveragemostly under pre-filtering on user-based CF, while item-basedshows stronger stability over the experimental conditions.
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