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Found 16 papers, 9 papers with code
Impression-Aware Recommender Systems
We present a systematic literature review on recommender systems using impressions, focusing on three fundamental angles in research: recommenders, datasets, and evaluation methodologies.
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.
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.
Artist-driven layering and user's behaviour impact on recommendations in a playlist continuation scenario
In this paper we provide an overview of the approach we used as team Creamy Fireflies for the ACM RecSys Challenge 2018.
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 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.
