The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with r...

I propose an analysis of verb phrases called Ground Promotion constructions (cf. McIntyre (2007)) or involving ’unpredicated particles’ (cf. Levin and Sells (2009)) at the syntaxsemantics interface. The interface framework combines principles of Minimalist Syntax and Distributed Morphology with Discourse Representation Theory. The analysis follows the hypothesis of a parallelism between the ver...

The coherent frequency (CF) of the auditory evoked response (AER) is derived using auditory clicks presented at frequencies in the range 5-47 Hz. CF and psychological performance were measured while seven subjects breathed isoflurane in doses increasing from 0% to 0.2%, 0.4% and 0.8% end-tidal concentration and then decreasing to 0%. With increasing doses of isoflurane, CF decreased and there w...

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 ...

Currently, implementations of the Collaborative Filtering (CF) algorithm are mostly centralized. Hence, information about the users, for example, product ratings, is concentrated in a single location. In this work we propose a novel approach to overcome the inherent limitations of CF (sparsity of data and cold start) by exploiting multiple distributed information repositories. These may belong ...

In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the dev...

The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF), mainly rely on the user-item rating matrix, which sometimes is not informative enough for predicting recommendations. To solve these challenges, the objective item attributes are incorporated as complementary informa...

Using only implicit data, many recommender systems fail in general to provide a precise set of recommendations to users with limited interaction history. This issue is regarded as the “Cold Start” problem and is typically resolved by switching to content-based approaches where extra costly information is required. In this paper, we use a dimensionality reduction algorithm, Word2Vec (W2V), origi...

The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach solve this problem is deploy an intelligent recommendation system on platforms. Collaborative filtering (CF) has shown its superiority by characterizing both items and latent features inferred from user–item interaction matrix. A fundamental challenge ...