The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A CF algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is no...

Collaborative Filtering with Implicit Feedbacks (e.g., browsing or clicking records), named as CF-IF, is demonstrated to be an effective way in recommender systems. Existing works of CF-IF can be mainly classified into two categories, i.e., point-wise regression based and pairwise ranking based, where the latter one relaxes assumption and usually obtains better performance in empirical studies....

Collaborative filtering (CF) is one of the most successful and widely used methods of automated product recommendation domain [1, 2, 3]. However, cardinal scale generally used for representing the preference intensity is also ineffective owing to its increasing estimation errors. In this paper, we propose a new CF-based recommendation methodology that constructs an ordinal scale-based customer ...

In Recommender Systems research, algorithms are often characterized as either Collaborative Filtering (CF) or Content Based (CB). CF algorithms are trained using a dataset of user explicit or implicit preferences while CB algorithms are typically based on item profiles. These approaches harness very different data sources hence the resulting recommended items are generally also very different. ...

Current mobile guides often suffer from the following problems: a long knowledge acquisition process of recommending relevant Points of Interest (POIs), the lack of social navigation support, and the challenge of making implicit user-generated content (e.g., trajectories) useful. Collaborative filtering (CF) is a promising solution for these problems. This article employs CF to mine GPS traject...

In many personalised recommendation problems, there are examples of items users prefer or like, but no examples of items they dislike. A state-of-the-art method for such implicit feedback, or one-class collaborative filtering (OC-CF), problems is SLIM, which makes recommendations based on a learned item-item similarity matrix. While SLIM has been shown to perform well on implicit feedback tasks...

Until recently, most research on cognitive processes and drug abuse has focused on theories and methods of explicit cognition. When explicit cognition is assessed, people are asked directly to introspect about the causes of their behavior, usually through traditional questionnaires. It may be questioned, however, to what extent such methods reflect fundamental aspects of human cognition and mot...

It has been suggested that the origins of cognitive modernity in the Middle/Upper Paleolithic following the appearance of anatomically modern humans was due to the onset of dual processing or contextual focus (CF), the ability to shift between different modes of thought: an explicit mode conducive to logical problem solving, and an implicit mode conducive to free-association and breaking out of...

The detection of low gas concentrations from the soil surface demands expensive high-precision devices to estimate nitrous oxide (N2O) flux. As prevalence N2O concentration in atmosphere is higher than its surface, present study aimed simulate flux (CF) measured a soil-interred silicone diffusion cell using low-cost device. methodological steps included determination coefficient membrane (Dslcn...

Collaborative Filtering (CF) is a popular method for personalizing product recommendations for e-Commerce and customer relationship management (CRM). CF utilizes the explicit or implicit product evaluation ratings of customers to develop personalized recommendations. However, there has been no in-depth investigation of the parameters of CF in relation to the number of ratings on the part of an ...