Many recommendation algorithms suffer from popularity bias in their output: popular items are recommended frequently and less popular ones rarely, if at all. However, less popular, long-tail items are precisely those that are often desirable recommendations. In this paper, we introduce a flexible re... || Recommender systems; long-tail; Recommendation evaluation; Coverage; Learning to rank; 长尾；diversity；多样性; || Himan Abdollahpouri; Robin Burke; Bamshad Mobasher...
Accurate model-based Collaborative Filtering (CF) approaches, such as Matrix Factorization (MF), tend to be black-box machine learning models that lack interpretability and do not provide a straightforward explanation for their outputs. Yet explanations have been shown to improve the transparency of... || factorization model; explainability; 根据近邻进行解释; || Behnoush Abdollahi...
Recommender Systems (RSs) in particular the collaborative filtering approaches have reached a high level of popularity. These approaches are designed for predicting the user’s future interests towards unrated items. However, the provided predictions should be taken with restraint because of the unce... || Recommender systems · Collaborative filtering · Belief function theory · Uncertainty · Evidential K-Nearest Neighbors；Dempster–Shafer theory; DS证据理论； 证据合成规则; || Raoua Abdelkhalek...
Collaborative Filtering (CF) consists of filtering data, predicting users’ preferences and providing recommendations accordingly. Commonly, neighborhood-based CF methods predict the future ratings based on similar users (user-based) or similar items (item- based) to perform recommendations. However... || Dempster–Shafer theory; DS证据理论； 证据合成规则; || Raoua Abdelkhalek...
Many machine learning systems utilize latent factors as internal representations for making predictions. Since these latent factors are largely uninterpreted, however, predictions made using them are opaque. Collaborative filtering via matrix factorization is a prime example of such an algorithm tha... || 隐因子解释; interpretation; shadow model; Quantitative input influence; QII; Interpreting； 仿真实验; simulation; mapping; || Anupam Datta, Sophia Kovaleva, Piotr Mardziel, Shayak Sen...
Matrix factorization has proven to be one of the most accurate recommendation approach. However, it faces one main shortcoming: the latent features that result from factoriza- tion, and that represent the underlying relation between users and items, are not directly interpretable. Some works focused... || representative users; interpretable; cold-start problem; ; || Armelle Brun; Marharyta Aleksandrova...
Matrix factorization (MF) is a powerful approach used in recommender systems. One main drawback of MF is the difficulty to interpret the automatically formed features. Following the intuition that the relation between users and items can be expressed through a reduced set of users, referred to as re... || Recommender systems, matrix factorization, features interpretation.; || Marharyta Aleksandrova...
The sparsity of users’ preferences can significantly degrade the quality of recommendations in the collaborative filtering strategy. To account for the fact that the selections of social friends and foes may improve the recommendation accuracy, we propose a learning to rank model that exploits users... || Learning to rank; social relationships; collaborative filtering; trust-based; || Dimitrios Rafailidis...
The iterative continuum of scientific production generates a need for filtering and specific crossing of ideas and papers. In this paper, we present BIBLME RecSys software which is dedicated to the analysis of bibliographical references extracted from scientific collections of papers. Our goal is to... || 【随意看】Recommender systems, Text mining, Digital libraries, Bib- liographic information, Bibliometrics.; || Anaı̈s Ollagnier, Sébastien Fournier, and Patrice Bellot...
