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Recommender systems are intrinsically tied to a reliability/coverage dilemma: The more reliable we desire the forecasts, the more conservative the decision will be and thus, the fewer items will be recommended. This causes a detriment to the ...

• Adjusting the variance in probability-based recommendation systems modifies the shape of the generated probabilities.
• High variance produces many low-precision predictions, while low variance yields fewer high-precision predictions.

Students’ perception of excessive difficulty in STEM degrees lowers their motivation and, therefore, affects their performance. According to prior research, the use of gamification techniques promote engagement, motivation, and fun when learning. ...

Recommendation to groups of users is a challenging subfield of recommendation systems. Its key concept is how and where to make the aggregation of each set of user information into an individual entity, such as a ranked recommendation list, a ...

Deep learning provides accurate collaborative filtering models to improve recommender system results. Deep matrix factorization and their related collaborative neural networks are the state of the art in the field; nevertheless, both models lack ...

In the context of recommender systems based on collaborative filtering (CF), obtaining accurate neighborhoods of the items of the datasets is relevant. Beyond particular individual recommendations, knowing these neighbors is fundamental for adding ...

Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors which do not incorporate semantic knowledge. Extracting the existing ...

This paper starts proposing a complete recommender system implemented on reconfigurable hardware with the purpose of testing on-chip, low-energy embedded collaborative filtering applications. Although the computing time is lower than the one ...

This Research Full Paper presents an empirical study about the motivation of Computer Science Engineering students. It is well known that student´s performance is affected by the academic conditions of the students, as well as their aptitudes and ...

Recommender systems based on collaborative filtering are widely used to predict users’ behaviour in large databases, where users rate items. The prediction model is built from a training dataset according to matrix factorization method and ...

Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem. A large number of researchers have published hundreds of papers to improve different RS features. It is advisable to use RS frameworks that simplify RS ...

Users want to know the reliability of the recommendations; they do not accept high predictions if there is no reliability evidence. Recommender systems should provide reliability values associated with the predictions. Research into reliability measures ...

Recommender Systems are designed to provide recommendations to registered users. Non-registered users can be regarded as a particular case of the pure new user cold-start problem. Since non-registered users have neither created a profile account nor ...

4D printing is a technology that combines the capabilities of 3D printing with materials that can transform its geometry after being produced (e.g. Shape Memory Polymers). These advanced materials allow shape change by applying different stimulus ...

Group recommender systems are becoming very popular in the social web owing to their ability to provide a set of recommendations to a group of users. Several group recommender systems have been proposed by extending traditional KNN based Collaborative ...

In this paper we present a novel technique for predicting the tastes of users in recommender systems based on collaborative filtering. Our technique is based on factorizing the rating matrix into two non negative matrices whose components lie within the ...

The explanation of and justification for recommendation results are important objectives in recommender systems because such explanations and justifications strongly influence the user's trust in the system. Traditional justification methods are based ...

In this paper we provide a method that allows the visualization of similarity relationships present between items of collaborative filtering recommender systems, as well as the relative importance of each of these. The objective is to offer visual ...

A significant number of recommender systems utilize the k-nearest neighbor (kNN) algorithm as the collaborative filtering core. This algorithm is simple; it utilizes updated data and facilitates the explanations of recommendations. Its greatest ...

In this paper, we present a novel technique for explaining the recommendations made by recommender systems based on collaborative filtering. Our technique is based on the visualisation of trees of items, and it provides users with a quick and attractive ...

Recommender systems are a type of solution to the information overload problem suffered by users of websites that allow the rating of certain items. The collaborative filtering recommender system is considered to be the most successful approach, as it ...