Assessing the Influences of Trip Similarity and User Relationships on Individual Travel Recommendations - Schlick, Sabine; Eigner, Isabella; Ensslen, Sabine (2015)
People often ask others for advice before making a decision. Especially in the context of travel planning, this leads to an increasing importance of communities. By conducting a study with members of an online travel community, this paper analyzes human recommendation behavior within a travel context. First, the concept of “similarity of trips”, a measure used by content-based recommender systems, is evaluated. Second, the role of friendships within a recommendation network is examined. Findings show that people tend to rate trips better if they have a high similarity to trips they did in the past. Additionally, people prefer trips recommended by their friends, even more so if a friend did this trip in the past. Finally, the results show that automatically recommended trips of the participants’ friends are accepted at least as well as user-generated recommendations for trips of friends and better than user-generated recommendations for trips of non-friend
Keywords: Travel recommendations; content-based filtering; user-generated recommendations

Discovering Critical Situations in Online Social Networks - A Neuro Fuzzy Approach to Alert Marketing Managers - Kaiser, Carolin; Bodendorf, Freimut; Schlick, Sabine (Proceedings of the 2nd International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2010)
More and more people are exchanging their opinions in online social networks and influencing each other. It is crucial for companies to observe opinion formation concerning their products. Thus, risks can be recognized early on and counteractive measures can be initiated by marketing managers. A neuro fuzzy approach is presented which allows the detection of critical situations in the process of opinion formation and the alerting of marketing managers. Rules for identifying critical situations are learned on the basis of the opinions of the network members, the influence of the opinion leaders and the structure of the network. The opinions and characteristics of the network are identified by text mining and social network analysis. The approach is illustrated by an exemplary application.
WiSo-Import:2015-04-17:701
Keywords: Innovation

Frühwarnsystem zur Identifikation kritischer Situationen der Meinungsbildung im Web 2.0 - Kaiser, Carolin; Schlick, Sabine (Wirtschaftsinformatik Proceedings 2011, 2011)
WiSo2-Import:2015-10-02:492

Optimierung automatisierter Empfehlungen auf Basis von menschlichem Empfehlungsverhalten: Eine Studie am Beispiel von Reiseempfehlungen - Schmidt, Tanja; Mayer, Sara; Schlick, Sabine (2013)
WiSo2-Import:2015-10-07:602

Using Collective Intelligence to Generate Trend-based Travel Recommendations - Schlick, Sabine; Eigner, Isabella; Fechner, Alex (Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015)
Trips are multifaceted, complex products which cannot be tested in advance due to their geographical distance. Hence, making a travel decision people often ask others for advice. This leads to an increasing importance of communities. Within communities people share their experiences, which results in new, more extensive knowledge beyond the individual knowledge of each member. The objective of this paper is to use this knowledge by developing an algorithm that automatically generates trend-based travel recommendations. Based on the travel experiences of the community members, interesting travel areas are identified. Five key figures to evaluate these areas according to general criteria and the users' individual preferences are developed. The algorithm allows to generate recommendations for the whole community and not only for highly active members, resulting in a high coverage. A study conducted within an online travel community shows that automatically generated, trend-based trip recommendations are rated better than user-generated recommendations.<br

Warning system for online market research - Identifying critical situations in online opinion formation - Kaiser, Carolin; Schlick, Sabine; Bodendorf, Freimut (Knowledge-Based Systems, 2011)
More and more consumers are relying on online opinions when making purchasing decisions. For this reason, companies must have knowledge of the actual standing of their products on the Web. A warning system for online market research is being proposed which allows the identification of critical situations in online opinion formation. When critical situations are detected, warnings are subsequently sent to marketing managers and thus allowing marketers the ability to initiate preventive measures. The warning system operates on a knowledge base which contains product-related success values, online opinions and patterns of social interactions. This knowledge is acquired using methods coming from information extraction, text mining and social network analysis. Based on this knowledge the warning system judges situations accordingly. For this purpose, a neuro-fuzzy approach is chosen which learns linguistic rules from data. These rules are employed to estimate future situations. The warning system is applied to two scenarios and yields good results. An evaluation shows that all components of the warning system outperform alternative methods. © 2011 Elsevier B.V. All rights reserved.
WiSo-Import:2015-03-26:713
Keywords: Innovation