Empirical Evaluation of User Models and User-Adapted Systems

By David N. Chin. User modeling and user-adapted interaction, 2001

298 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Adaptive interfaces and agents (2002, methodology): “6Han and Kamber (2001), Langley (1996), and Mitchell (1997) offer broad overviews of statistical and machine learning techniques, some of which are related to those discussed in this section. The role of machine learning in UASs is reviewed by Webb, Pazzani, and Billsus (2001).”
Evaluation of contextual information retrieval effectiveness: overview of issues and research (2010, background): “In order to overcome such problems, several recommendations are given to ensure the reliability of experimental ﬁndings issued from user studies [12,21]: – randomly assign enough user participants to groups; each group could be identiﬁed as being related to the same task situation (called also…”
Evaluating Recommender Systems with User Experiments (2015, background): “On the other hand, recommender systems are definitely not always better than their non-personalized variant, so a comparison with a non-personalized system may very well be justified when testing a recommender in a new domain [21].”
Explaining the user experience of recommender systems (2012, background): “However, Chin (2001) argues that this advantage is not always apparent and that a comparison with a non-personalized system may very well be justified.”
Drivers of consumers’ willingness to pay for halal logistics (2016, background): “80 indicating that the power of the current sample is satisfactory (Chin, 2001).”