78,663 research outputs found
What do implicit measures measure?
We identify several ongoing debates related to implicit measures, surveying prominent views and considerations in each debate. First, we summarize the debate regarding whether performance on implicit measures is explained by conscious or unconscious representations. Second, we discuss the cognitive structure of the operative constructs: are they associatively or propositionally structured? Third, we review debates whether performance on implicit measures reflects traits or states. Fourth, we discuss the question of whether a person’s performance on an implicit measure reflects characteristics of the person who is taking the test or characteristics of the situation in which the person is taking the test. Finally, we survey the debate about the relationship between implicit measures and (other kinds of) behavior
Recommender Systems with Characterized Social Regularization
Social recommendation, which utilizes social relations to enhance recommender
systems, has been gaining increasing attention recently with the rapid
development of online social network. Existing social recommendation methods
are based on the fact that users preference or decision is influenced by their
social friends' behaviors. However, they assume that the influences of social
relation are always the same, which violates the fact that users are likely to
share preference on diverse products with different friends. In this paper, we
present a novel CSR (short for Characterized Social Regularization) model by
designing a universal regularization term for modeling variable social
influence. Our proposed model can be applied to both explicit and implicit
iteration. Extensive experiments on a real-world dataset demonstrate that CSR
significantly outperforms state-of-the-art social recommendation methods.Comment: to appear in CIKM 201
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
- …