GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Using information scent to model user information needs and actions and the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Effects of Negative Information on Acquiring Procedural Knowledge
ICCE '02 Proceedings of the International Conference on Computers in Education
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning a model of a web user's interests
UM'03 Proceedings of the 9th international conference on User modeling
Personalizing web search using long term browsing history
Proceedings of the fourth ACM international conference on Web search and data mining
Personalized Searching for Web Service Using User Interests
DASC '11 Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing
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Recommender systems are a common solution used to assist users in searching and retrieving information on the web due to the benefits that can be obtained from the evaluation and filtering of the vast amount of information available. This article presents a user study on the feasibility of using negative interaction, that is the absence of interaction with some items in a list of suggestions, as implicit feedback used to improve the performance of a web navigation assistant. Results showed an increment of 16.65% in the acceptance of the suggestions provided by the assistant and an increment of 43.05% in the average use of the suggestions window when using negative interaction with respect to not using this feedback mechanism.