Abstract
Investigators publish new report on computational intelligence. According to news reporting from Tokyo, Japan, by NewsRx journalists, research stated, “This paper proposes a personal values modeling method that does not require attribute ratings.” Funders for this research include Japan Society For The Promotion of Science. Our news editors obtained a quote from the research from Tokyo Metropolitan University: “The proposed method is applied to memory-based and model-based collaborative filtering (CF) to demonstrate its effectiveness. A recent trend in CF is to introduce additional factors than interaction history. A rate matching rate (RMRate) has been proposed for modeling user’s personal values, and it has been shown to be effective in increasing diversity and recommending niche (long-tail or unpopular) items. However, RMRate needs an attribute-level evaluations in addition to rating (total evaluation) to items, which limits its applicability. To obtain users’ personal values model only from a rating matrix, this paper defines users’ personal values as their tendency to select popular/unpopular items and reputable/unreputable items. Ten attributes are proposed to model user’s personal values, all of which can be calculated from a rating matrix without additional information.”
NETL
NSTL
Fuji Technology Press Ltd