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Incorporating Recklessness to Collaborative Filtering based Recommender Systems Open
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 t…
New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering Open
In recent times, recommender systems (RSs) have been attracting a lot of attention from the research community because of their groundbreaking applications [...]
Deep Neural Aggregation for Recommending Items to Group of Users Open
Modern society devotes a significant amount of time to digital interaction. Many of our daily actions are carried out through digital means. This has led to the emergence of numerous Artificial Intelligence tools that assist us in various …
Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems. Open
Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tes…
An evaluation framework for dimensionality reduction through sectional curvature Open
Unsupervised machine learning lacks ground truth by definition. This poses a major difficulty when designing metrics to evaluate the performance of such algorithms. In sharp contrast with supervised learning, for which plenty of quality me…
Using 3D printed badges to improve student performance and reduce dropout rates in STEM higher education Open
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 learn…
Neural Group Recommendation Based on a Probabilistic Semantic Aggregation Open
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…
Deep variational models for collaborative filtering-based recommender systems Open
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 model…
1293 Establishing the preclinical PKPD relationship for NM32–2668 a ROR1 targeting T cell engager Open
Background NM32-2668 is a fragment-based multispecific antibody therapeutic1 that has been designed to activate T-cells (via CD3) in the presence of tumour antigen receptor tyrosine kinase-like orphan receptor 1 (ROR1). The objective of th…
Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering Open
Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial intelligence. Therefore, those models that are able to provide not only predictions, but also re…
Deep Variational Embedding Representation on Neural Collaborative Filtering Recommender Systems Open
Visual representation of user and item relations is an important issue in recommender systems. This is a big data task that helps to understand the underlying structure of the information, and it can be used by company managers and technic…
Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System Open
Traditionally, recommender systems have been approached as regression models aiming to predict the score that a user would give to a particular item. In this work, we propose a recommender system that tackles the problem as a classificatio…
Bias and Unfairness of Collaborative Filtering Based Recommender Systems in MovieLens Dataset Open
Recommender Systems have become one of the most important tools for streaming and marketplace systems in recent years. Their increased use has revealed clear bias and unfairness against minorities and underrepresented groups. This paper se…
826 Establishing the preclinical/translational PK/PD relationship for BT7480, a nectin-4/CD137 <i>Bicycle</i> tumor-targeted immune cell agonist™ (<i>Bicycle</i> TICA™) Open
Background A new class of modular synthetic drugs, termed Bicycle tumor-targeted immune cell agonists (Bicycle TICAs), based on constrained bicyclic peptides has been developed as agonists of immune costimulatory receptors in cancer therap…
Deep learning approach to obtain collaborative filtering neighborhoods Open
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 fo…
Deep Variational Models for Collaborative Filtering-based Recommender Systems Open
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-art in the field; nevertheless, both models la…
LEGO® Serious Play in Software Engineering Education Open
Nowadays, it is mandatory to complement the traditional learning methods with active ones that enhance the student's motivation and facilitate the development of technical and soft competences. LEGO®Serious Play is an experiential methodol…
Recommender Systems and Collaborative Filtering Open
Recommender Systems (RSs) have become an essential tool for the information society [...]
Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks Open
Internet of Things (IoT) projects are increasing in size over time, and some of them are growing to reach the whole world. Sensor arrays are deployed world-wide and their data is sent to the cloud, making use of the Internet. These huge ne…
Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems Open
Providing useful information to the users by recommending highly demanded products and services is a fundamental part of the business of many top tier companies. Recommender Systems make use of many sources of information to provide users …
Performance of Two Approaches of Embedded Recommender Systems Open
Nowadays, highly portable and low-energy computing environments require programming applications able to satisfy computing time and energy constraints. Furthermore, collaborative filtering based recommender systems are intelligent systems …
Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems. Open
This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ratings dataset. …
Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming Open
Recommender systems aim to estimate the judgment or opinion that a user might offer to an item. Matrix-factorization-based collaborative filtering typifies both users and items as vectors of factors inferred from item rating patterns. This…
View article: PF224 NOVEL ICMT INHIBITOR AS POTENTIAL TREATMENT OF RAS‐DRIVEN ACUTE MYELOID LEUKEMIA
PF224 NOVEL ICMT INHIBITOR AS POTENTIAL TREATMENT OF RAS‐DRIVEN ACUTE MYELOID LEUKEMIA Open
Background: RAS overexpression and activation is a common leukemogenic mechanism in acute myeloid leukemia (AML), which is characterized by HRAS, NRAS and KRAS mutations. However, after several decades of continuous research efforts, RAS d…
A Collaborative Filtering Approach Based on Naïve Bayes Classifier Open
Recommender system is an information filtering tool used to alleviate information overload for users on the web. Collaborative filtering recommends items to users based on their historical rating information. There are two approaches: memo…
Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems Open
As the use of recommender systems becomes generalized in society, the interest in varying the orientation of their recommendations is increasing. There are shilling attacks’ strategies that introduce malicious profiles in collaborative fil…
A New Recommendation Approach Based on Probabilistic Soft Clustering Methods: A Scientific Documentation Case Study Open
Recommender system (RS) clustering is an important issue, both for the improvement of the collaborative filtering (CF) accuracy and to obtain analytical information from their high sparse datasets. RS items and users usually share features…