Multimedia content is dominating today’s Web information. Œe nature of multimedia user-item interactions is 1/0 binary implicit feedback (e.g., photo likes, video views, song downloads, etc.), which can be collected at a larger scale with a much lower cost than explicit feedback (e.g., product ratings). However, the majority of existing collaborative €ltering (CF) systems are not well-designed ...

In many personalized recommendation problems available data consists only of positive interactions (implicit feedback) between users and items. This problem is also known as One-Class Collaborative Filtering (OC-CF). Linear models usually achieves state-of-the-art performances on OC-CF problems and many efforts have been devoted to build more expressive and complex representations able to impro...

Most collaborative filtering (CF) models estimate missing ratings with an implicit assumption that the are missing-at-random, which may cause biased rating estimation and degraded performance since recent deep exploration shows likely be missing-not-at-random (MNAR). To debias MNAR estimation, we introduce item observability user selection to depict generation of propose a tripartite CF (TCF) f...

Recommender system plays an increasingly important role in identifying the individual’s preference and accordingly makes a personalized recommendation. Matrix factorization is currently most popular model-based collaborative filtering (CF) method that achieves high recommendation accuracy. However, similarity computation hinders development of CF-based systems. Preference obtained only depends ...

In recommender systems (RSs), explicit information is often preferred over implicit because it much more accurate than or predicted information; for example, the user can enter about his interests directly into system, and system will generate recommendations him. Receiving information, however, may be difficult a system. Explicit demographic might uncomfortable some users, extremely common que...

Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However,...

Since the advent of the Netflix Prize [1], there has been an influx of papers on recommender systems in machine learning literature. A popular framework to build such systems has been collobarative filtering (CF) [6]. On the Netflix dataset, CF algorithms were one of the few stand-alone methods shown to have superior performance. Recently, web services such as Foursquare and Facebook Places sta...

Based on the user-item bipartite network, collaborative filtering (CF) recommender systems predict users’ interests according to their history collections, which is a promising way to solve the information exploration problem. However, CF algorithm encounters cold start and sparsity problems. The trust-based CF algorithm is implemented by collecting the users’ trust statements, which is time-co...

It is worthwhile to search for forms of coding, processing, and learning common to various cortical regions and cognitive functions. Local cortical processors may coordinate their activity by maximizing the transmission of information coherently related to the context in which it occurs, thus forming synchronized population codes. This coordination involves contextual field (CF) connections tha...