Hypothetical tensor-based multi-criteria recommender system for new users with partial preferences

Multi-Criteria Recommender Systems (MCRSs) have been developed to improve the accuracy of single-criterion rating-based recommender systems that could not express and reflect users? fine-grained rating behaviors. In most MCRSs, new users are asked to express their preferences on multi-criteria of items, to ad15 dress the cold-start problem. However, some of the users? preferences collected are usually not complete due to users? cognitive limitation and/or unfamiliarity on item domains, which is called ?partial preferences?. The fundamental challenge and then negatively affects to accurately recommend items according to users? preferences through MCRSs. In this paper, we propose a Hypothetical Tensor Model (HTM) to leverage auxiliary data complemented through three intuitive rules dealing with user?s unfamiliarity. First, we find four patterns of partial preferences that are caused by users? unfamiliarity. And then the rules are defined by considering relationships between multi-criteria. Lastly, complemented preferences are modeled by a tensor to maintain an inherent structure of and correlations between the multi-criteria. Experiments on a TripAdvisor dataset showed that HTM improves MSE performances from 40 to 47% by comparing with other baseline methods. In particular, effective nesses of each rule regarding multi-criteria on HTM are clearly revealed.

PLACING PROBABILITIES OF CONDITIONALS IN CONTEXT

In this paper I defend the tenability of the Thesis that the probability of a conditional equals the conditional probability of the consequent given the antecedent. This is done by adopting the view that the interpretation of a conditional may differ from context to context. Several triviality results are (re-)evaluated in this view as providing natural constraints on probabilities for conditionals and admissible changes in the interpretation. The context-sensitive approach is also used to re-interpret some of the intuitive rules for conditionals and probabilities such as Bayes’ rule,Import-Export, and Modus Ponens. I will show that, contrary to consensus, the Thesis is in fact compatible with these re-interpreted rules.

The use of intuitive rules in interpreting students’ difficulties in reading and creating kinematic graphs

The ability to form and interpret kinematic graphs is an essential skill for students studying physics. The literature, however, details a variety of students’ difficulties in this regard. The present article discusses how intuitive rules theory (Stavy and Tirosh. Int. J. Sci. Educ. 18, 653 (1996)) can be applied as a conceptual framework for understanding why some of these difficulties may occur. It suggests, moreover, that explicit teaching regarding students’ use of intuitive rules in interpreting kinematic graphs may deepen students’ general understanding of graphs in physics.