Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2016

Atzmüller Martin, Alvin Chin, Trattner Christoph

Proceedings of the 7th International Workshop on Modeling Social Media (MSM’16) at the 25th ACM World Wide Web Conference WWW’16 conference

ACM WWW2016, ACM, Montreal, Canada, 2016

Buch
2016

Trattner Christoph, Kuśmierczyk Tomasz, Rokicki Markus, Herder Eelco

Plate and Prejudice: Gender Differences in Online Cooking

UMAP 2016, ACM, Halifax, NS, Canada , 2016

Konferenz
Historically, there have always been differences in how men andwomen cook or eat. The reasons for this gender divide have mostlygone in Western culture, but still there is qualitative and anecdotalevidence that men prefer heftier food, that women take care of everydaycooking, and that men cook to impress. In this paper, weshow that these differences can also quantitatively be observed in alarge dataset of almost 200 thousand members of an online recipecommunity. Further, we show that, using a set of 88 features, thegender of the cooks can be predicted with fairly good accuracy of75%, with preference for particular dishes, the use of spices andthe use of kitchen utensils being the strongest predictors. Finally,we show the positive impact of our results on online food reciperecommender systems that take gender information into account.
2016

Trattner Christoph, Kuśmierczyk Tomasz, Nørvåg Kjetil

FOODWEB - Studying Online Food Consumption and Production Patterns on the Web

ERCIM NEWS, ERCIM EEIG, 2016

Journal
2016

Trattner Christoph, Elsweiler David, Howard Simon

Estimating the Healthiness of Internet recipes: Implications for Recommender Systems and Meal Planning

British Medical Journal (BMJ) , Frontiers in Public Health, 2016

Konferenz
One government response to increasing incidence of lifestyle related illnesses, such as obesity, has been to encourage people to cook for themselves. The healthiness of home cooking will, nevertheless, depend on what people cook and how they cook it. In this article one common source of cooking inspiration - Internet-sourced recipes - is investigated in depth. The energy and macronutrient content of 5237 main meal recipes from the food website Allrecipes.com are compared with those of 100 main meal recipes from five bestselling cookery books from popular celebrity chefs and 100 ready meals from the three leading UK supermarkets. The comparison is made using nutritional guidelines published by the World Health Organisation and the UK Food Standards Agency. The main conclusions drawn from our analyses are that Internet recipes sourced from Allrecipes.com are less healthy than TV-chef recipes and ready meals from leading UK supermarkets. Only 6 out of 5237 Internet recipes fully complied with the WHO recommendations. Internet recipes were more likely to meet the WHO guidelines for protein than both other classes of meal (10.88% v 7% (TV), p<0.01; 10.86% v 9% (ready), p<0.01). However, the Internet recipes were less likely to meet the criteria for fat (14.28% v 24% (TV) v 37% (ready); p<0.01), saturated fat (25.05% v 33% (TV) v 34% (ready); p<0.01) and fibre (compared to ready meals 16.50% v 56%; p<0.01). More Internet recipes met the criteria for sodium density than ready meals (19.63% v 4%; p<0.01), but fewer than the TV-chef meals (19.32% v 36%; p<0.01). For sugar, no differences between Internet recipes and TV-chef recipes were observed (81.1% v 81% (TV); p=0.86), although Internet recipes were less likely to meet the sugar criteria than ready meals (81.1% v 83 % (ready); p<0.01). Repeating the analyses for each year of available data shows that the results are very stable over time.
2016

Trattner Christoph, Schäfer Hanna, Said Alan, Ludwig Bernd, Elsweiler David

Proceedings of the International Workshop on Engendering Health

10th ACM Conference on Recommender Systems, ACM, Boston, 2016

Buch
Busy lifestyles, abundant options, lack of knowledge ... there are many reasons why people make poor decisions relating to their health. Yet these poor decisions are leading to epidemics, which represent some of the greatest challenges we face as a society today. Noncommunicable Diseases (NCDs), which include cardiovascular diseases, cancer, chronic respiratory diseases and diabetes, account for ∼60% of total deaths worldwide. These diseases share the same four behavioural risk factors: tobacco use, unhealthy diet, physical inactivity and harmful consumption of alcohol and can be prevented and sometimes even reversed with simple lifestyle changes. Eating more healthily, exercising more appropriately, sleeping and relaxing more, as well as simply being more aware of one’s state of health are all things that would lead to improved health. Yet knowing exactly what to change and how, implementing changes and maintaining changes over long time periods are all things people find challenging. These are also problems, for which we believe recommender systems can provide assistance by offering specific, tailored suggestions for behavioural change. In recent years recommender systems for health has become a popular topic within the RecSys community and a selection of empirical contributions and demo systems have been published. Efforts to date, however have been sporadic and lack coordination. We lack shared infrastructure such as datasets, appropriate cross-disciplinary knowledge, even agreed upon goals. It is our aim to use this workshop as a vehicle to:
2016

Atzmüller Martin, Chin Alvin, Trattner Christoph

Proceedings of the 7th International Workshop on Modeling Social Media

25th International World Wide Web Conference, MSM 2017, Montreal, 2016

Buch
For the 7h International Workshop on Modeling Social Media, we aim to attract researchers from all over the world working in the field of behavioral analytics using web and social media data. Behavioral analytics is an important topic, e.g., concerning web applications as well as extensions in mobile and ubiquitous applications, for understanding user behavior. We would also like to invite researchers in the data and web mining community to lend their expertise to help to increase our understanding of the web and social media.
2016

Eberhard Lukas, Trattner Christoph

Recommending Sellers to Buyers in Virtual Marketplaces Leveraging Social Information

WWW '16 Companion Proceedings of the 25th International Conference Companion on World Wide Web, WWW '16, Canton of Geneva, 2016

Konferenz
Social information such as stated interests or geographic check-insin social networks has shown to be useful in many recommendertasks recently. Although many successful examples exist, not muchattention has been put on exploring the extent to which social im-pact is useful for the task of recommending sellers to buyers in vir-tual marketplaces. To contribute to this sparse field of research wecollected data of a marketplace and a social network in the virtualworld of Second Life and introduced several social features andsimilarity metrics that we used as input for a user-basedk-nearestneighbor collaborative filtering method. As our results reveal, mostof the types of social information and features which we used areuseful to tackle the problem we defined. Social information suchas joined groups or stated interests are more useful, while otherssuch as places users have been checking in, do not help much forrecommending sellers to buyers. Furthermore, we find that some ofthe features significantly vary in their predictive power over time,while others show more stable behaviors. This research is rele-vant for researchers interested in recommender systems and onlinemarketplace research as well as for engineers interested in featureengineering.
2016

Trattner Christoph, Oberegger Alexander, Eberhard Lukas, Parra Denis, Marinho Leandro

Understanding the Impact of Weather for POI recomennder systems

RecTour’16,, ACM, Boston, 2016

Konferenz
POI (point of interest) recommender systems for location-based social network services, such as Foursquare or Yelp,have gained tremendous popularity in the past few years.Much work has been dedicated into improving recommenda-tion services in such systems by integrating different featuresthat are assumed to have an impact on people’s preferencesfor POIs, such as time and geolocation. Yet, little atten-tion has been paid to the impact of weather on the users’final decision to visit a recommended POI. In this paper wecontribute to this area of research by presenting the firstresults of a study that aims to predict the POIs that userswill visit based on weather data. To this end, we extend thestate-of-the-art Rank-GeoFM POI recommender algorithmwith additional weather-related features, such as tempera-ture, cloud cover, humidity and precipitation intensity. Weshow that using weather data not only significantly increasesthe recommendation accuracy in comparison to the origi-nal algorithm, but also outperforms its time-based variant.Furthermore, we present the magnitude of impact of eachfeature on the recommendation quality, showing the need tostudy the weather context in more detail in the light of POIrecommendation systems.
2016

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

Konferenz
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.
2016

Trattner Christoph, Kowald Dominik, Seitlinger Paul, Ley Tobias

Modeling Activation Processes in Human Memory to Predict the Reuse of Tags

The Journal of Web Science, James Finlay, NOW publishing, 2016

Journal
Several successful tag recommendation mechanisms have been developed, including algorithms built upon Collaborative Filtering, Tensor Factorization, graph-based and simple "most popular tags" approaches. From an economic perspective, the latter approach has been convincing since calculating frequencies is computationally efficient and effective with respect to different recommender evaluation metrics. In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory in order to extend these conventional "most popular tags" approaches. Based on a theory of human memory, the approach estimates a tag's reuse probability as a function of usage frequency and recency in the user's past (base-level activation) as well as of the current semantic context (associative component).Using four real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike, Delicious and Flickr, we show how refining frequency-based estimates by considering recency and semantic context outperforms conventional "most popular tags" approaches and another existing and very effective but less theory-driven, time-dependent recommendation mechanism. By combining our approach with a simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as Collaborative Filtering, FolkRank and Pairwise Interaction Tensor Factorization with respect to recommender accuracy and runtime. We conclude that our approach provides an accurate and computationally efficient model of a user's temporal tagging behavior. Moreover, we demonstrate how effective principles of recommender systems can be designed and implemented if human memory processes are taken into account.
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