Recommender Systems (RSs) are a prominent solution to the problem of information overload on the web. It is impossible for users to process or even understand all information presented to them. Also, it becomes more and more difficult for an individual to identify appropriate concrete pieces of information or information sources. RSs aim at adapting the presented content and the order in which it is presented to users’ individual needs, based on their preferences and past behavior. Yet, a system can only provide accurate recommendations if it has been authentically used before, i.e., been able to collect information about a user. As authentic usage depends on a user’s acceptance, the success of RSs in general is strongly dependent on acceptance also. If recommendations seem inappropriate, the trust in the system will fade. This paper presents a study analyzing how and to what extent different factors like transparency or controllability influence acceptance in the context of web-based recommendation.
|Title of host publication||ABIS 2012 - Proceedings of the 19th International Workshop on Personalization and Recommendation on the Web and Beyond|
|Number of pages||8|
|Publication status||Published - 2012|
|Event||ABIS 2012 - 19th International Workshop on Personalization and Recommendation on the Web and Beyond - Konstanz, Germany|
Duration: 9 Sep 2012 → 9 Sep 2012
|Workshop||ABIS 2012 - 19th International Workshop on Personalization and Recommendation on the Web and Beyond|
|Period||09.09.2012 → 09.09.2012|