Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and interpretation. In this paper, we propose a novel hybrid recommendation strategy that refines CF by capturing these dynamics. The evaluation results reveal that our approach substantially improves CF and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant.
A specific research topic, which is increasingly gaining popularity, is human decision making in recommender systems @cite_12 . The work presented in @cite_7 systematically analyzes recommender systems as decision support systems based on the nature of users' goals and the dynamic characteristics of the resource space (e.g., availability of resources). However, there is still lack of research on investigating user decision processes at a detailed level and considering integrating scientific facts from psychology. Thus, we consider that our proposed approach contributes to this line of research.
@cite_7 @cite_12
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factors that influence users' decision making processes in Recommender Systems (RSs) have been investigated by a relatively vast research of empirical and theoretical nature, mostly in the field of e - commerce . In this paper, we discuss some aspects of the user experience with RSs that may affect the decision making process and outcome, and have been marginally addressed by prior research. These include the nature of users' goals and the dynamic characteristics of the resources space (e.g., availability during the search process) . We arg ue that these subjective and objective factors of the user experience with a RS call for a rethinking of the decision making process as it is normally assumed in traditional RSs, and raise a number or research challenges. These concepts are exemplified in the application domain of on - line services , specifically, hotel booking - a field where w e are carrying on a number of activities in cooperation with a large stakeholder ( Venere.com - a company of Expedia Inc.). Still, most of the arguments discussed in the paper can be extended to other domains, and have general implications for RS design and evaluation. Recommender systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest for them; hence, a primary function of recommender systems is to help people make good choices and decisions. However, most previous research has focused on recommendation techniques and algorithms, and less attention has been devoted to the decision making processes adopted by the users and possibly supported by the system. There is still a gap between the importance that the community gives to the assessment of recommendation algorithms and the current range of ongoing research activities concerning human decision making. Different decision-psychological phenomena can influence the decision making of users of recommender systems, and research along these lines is becoming increasingly important and popular. This special issue highlights how the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort.