Search Results for author:
Found 29 papers, 15 papers with code
Towards the Application of Calibrated Transformers to the Unsupervised Estimation of Question Difficulty from Text
They use questions of known difficulty to train models capable of inferring the difficulty of questions from their text.
On the application of Transformers for estimating the difficulty of Multiple-Choice Questions from text
Classical approaches to question calibration are either subjective or require newly created questions to be deployed before being calibrated.
Reinforcement Learning for Variational Quantum Circuits Design
Our results indicate that the $R_{yz}$-connected circuit achieves high approximation ratios for Maximum Cut problems, further validating our proposed agent.
Adaptive Learning for Quantum Linear Regression
Although this approach promises a computational time advantage for large datasets, the quality of the solution is limited by the necessary use of a precision vector, used to approximate the real-numbered regression coefficients in the quantum formulation.
Impression-Aware Recommender Systems
We define a theoretical framework to delimit recommender systems using impressions and a novel paradigm for personalized recommendations, called impression-aware recommender systems.
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances
In this paper, we aim to fill this gap by analyzing three Adaptative VQAs: Evolutionary Variational Quantum Eigensolver (EVQE), Variable Ansatz (VAns), already proposed in the literature, and Random Adapt-VQE (RA-VQE), a random approach we introduce as a baseline.
Feature Selection for Classification with QAOA
The removal of redundant and noisy features can improve both the accuracy and scalability of the trained models.
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers
By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced.
GAN-based Matrix Factorization for Recommender Systems
Through an ablation study on the components of GANMF we aim to understand the effects of our architectural choices.
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering
This work explores the reproducibility of CFGAN.
Feature Selection for Recommender Systems with Quantum Computing
The promise of quantum computing to open new unexplored possibilities in several scientific fields has been long discussed, but until recently the lack of a functional quantum computer has confined this discussion mostly to theoretical algorithmic papers.
Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels
A crucial aspect of this user interface is that to measure the relevance a new carousel for the user it is not sufficient to account solely for its individual quality.
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels
Hence, we propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels.
On the instability of embeddings for recommender systems: the case of Matrix Factorization
In this paper we question the reliability of the embeddings learned by Matrix Factorization (MF).
ContentWise Impressions: An Industrial Dataset with Impressions Included
In this article, we introduce the ContentWise Impressions dataset, a collection of implicit interactions and impressions of movies and TV series from an Over-The-Top media service, which delivers its media contents over the Internet.
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems
In recent years, algorithm research in the area of recommender systems has shifted from matrix factorization techniques and their latent factor models to neural approaches.
A Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations
Researchers proposed several techniques to search in the space of compiler optimisations.
Distributed, Parallel, and Cluster Computing Performance
Introducing a framework to assess newly created questions with Natural Language Processing
Statistical models such as those derived from Item Response Theory (IRT) enable the assessment of students on a specific subject, which can be useful for several purposes (e. g., learning path customization, drop-out prediction).
R2DE: a NLP approach to estimating IRT parameters of newly generated questions
The main objective of exams consists in performing an assessment of students' expertise on a specific subject.
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research
In our analysis, we discuss common issues in today's research practice, which, despite the many papers that are published on the topic, have apparently led the field to a certain level of stagnation.
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features
To raise awareness of this fact, we investigate differences between explicit user preferences and implicit user profiles.
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems.
Eigenvalue analogy for confidence estimation in item-based recommender systems
Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of the conditions under which item-based recommendations will succeed or fail.
A novel graph-based model for hybrid recommendations in cold-start scenarios
Some techniques to optimize performance of this type of approaches have been studied in recent past.
Sequence-Aware Recommender Systems
In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process.
Social Network based Short-Term Stock Trading System
This allows the algorithm to ensure the maximization of the obtainable profits by trading on the stock market.
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
Session-based recommendations are highly relevant in many modern on-line services (e. g. e-commerce, video streaming) and recommendation settings.
Using Mise-En-Scène Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation
In this paper, we show that user's preferences on movies can be better described in terms of the mise-en-sc\`ene features, i. e., the visual aspects of a movie that characterize design, aesthetics and style (e. g., colors, textures).
Sparse vs. Non-sparse: Which One Is Better for Practical Visual Tracking?
Recently, sparse representation based visual tracking methods have attracted increasing attention in the computer vision community.
