诺库维普

Controlling Popularity Bias in Learning-to-Rank Recommendation 2018-06-19 09:14:33

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...

Using Explainability for Constrained Matrix Factorization 2018-06-12 12:21:31

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...

Evidential Item-Based Collaborative Filtering 2018-06-09 23:19:20

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...

Improving the Trustworthiness of Recommendations in Collaborative Filtering under the Belief Function Framework 2018-06-09 16:35:11

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...

Latent Factor Interpretations for Collaborative Filtering 2018-06-04 14:01:14

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...

Can Latent Features be Interpreted as Users in Matrix Factorization-based Recommender Systems? 2018-06-02 15:50:44

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...

What about Interpreting Features in Matrix Factorization-based Recommender Systems as Users? 2018-06-02 14:25:59

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...

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Austin, Texas, USA,

AAAI 23

Washington, DC, USA,

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Madrid, Spain, July 11th to 15th, 2022

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Austria, July 23-29, 2022