Irina Heimbach

The Value of User's Facebook Profile Data for Product Recommendation Generation

Most online shops apply recommender systems, i.e. software agents that elicit the users’ preferences and interests with the purpose to make product recommendations. Many of these systems suffer from the new user cold start problem which occurs when no transaction history is available for the particular new prospective buyer. External data from social networking sites, like Facebook, seem promising to overcome this problem. In this paper, we evaluate the value of Facebook profile data to create meaningful product recommendations. We find based on the outcomes of a user experiment that already simple approaches and plain profile data matching yield significant better recommendations than a pure random draw from the product data base. However, the most successful approaches use semantic categories like music/video, brands and product category information to match profile and product data. A second experiment indicates that recommendation quality seems to be stable for different profile sizes.

Content Virality on Online Social Networks: Empirical Evidence from Twitter, Facebook, and Google+ on German News Websites

The virality of content describes its likelihood to be shared with peers. In this work, we investigate how content characteristics impact the sharing likelihood of news articles on Twitter, Facebook, and Google+. We examine a random sample of 4,278 articles from the most popular news websites in Germany categorized by human classifiers and text mining tools. Our analysis reveals commonalities and subtle differences between the three networks indicating different sharing patterns of their users.

The Impact of Sharing Mechanism Design on Content Sharing in Online Social Networks

Research on online content diffusion is vast but has rarely examined contextual factors, including the influence of online sharing mechanisms, such as social plugins (e.g., Facebook’s “Like” button), on online social networks (OSNs). While these mechanisms generally enable the content flow between senders and recipients, they vary in protecting users’ social and institutional privacy on OSNs. Additionally, sharing mechanisms might differ with respect to their labeling (e.g., positive versus neutral), which might interact with the sharable content. We examined the effects of these three design aspects on users’ sharing behavior in a controlled experiment and two analyses of observational data. The results show that two types of sharing mechanisms negatively affect content sharing in the domain of news sharing: those that allow greater information flow control over the sharing process and thus protect users’ social privacy and those that employ two-click designs to preserve users’ institutional privacy. The...