Collaborative €ltering (CF) is one of themost ecient ways for recommender systems. Typically, CF-based algorithms analyze users’ preferences and items’ aŠributes using one of two types of feedback: explicit feedback (e.g., ratings given to item by users, like/dislike) or implicit feedback (e.g., clicks, views, purchases). Explicit feedback is reliable but is extremely sparse; whereas implicit ...

In this note, based on a control Lyapunov function approach, an integrated design of switching laws and feedback controllers for uncertain switched nonlinear control systems with two modes is discussed. A sufficient condition for the existence of globally asymptotically stabilizing state feedback laws (switching laws  controllers) is derived. Moreover, an explicit rule for constructing switchi...

Conceptual change represents a crucial, challenging, learning component. This study hypothesized and observed evidence for two parallel forms of learning within the Numerically-Driven Inferencing (NDI) paradigm’s rather minimalist intervention of providing direct feedback regarding a numerical estimate––feedback that yields remarkably robust cognitive alterations. The present experiment probed ...

In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update th...