Towards Interpretability of the Movie Recommender Based on a Neuro-Fuzzy Approach

Rutkowski, Tomasz M.; Romanowski, Jakub; Woldan, Piotr; Staszewski, Paweł; Nielek, Radosław

doi:10.1007/978-3-319-91262-2_66

Cited by 11 publications

(3 citation statements)

References 21 publications

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“…[26,36]), and entire methodology of machine learning like neural networks, fuzzy sets, genetic algorithms (e.g. [5,45,46]). Nonparametric approach to analysis and modelling of various systems one may found e.g.…”

Section: A Brief Overview Of the Methodologies Used To Datementioning

confidence: 99%

A New Approach to Detection of Changes in Multidimensional Patterns

Gałkowski

Krzyżak

Filutowicz

2020

Journal of Artificial Intelligence and Soft Computing Research

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Nowadays, unprecedented amounts of heterogeneous data collections are stored, processed and transmitted via the Internet. In data analysis one of the most important problems is to verify whether data observed or/and collected in time are genuine and stationary, i.e. the information sources did not change their characteristics. There is a variety of data types: texts, images, audio or video files or streams, metadata descriptions, thereby ordinary numbers. All of them changes in many ways. If the change happens the next question is what is the essence of this change and when and where the change has occurred. The main focus of this paper is detection of change and classification of its type. Many algorithms have been proposed to detect abnormalities and deviations in the data. In this paper we propose a new approach for abrupt changes detection based on the Parzen kernel estimation of the partial derivatives of the multivariate regression functions in presence of probabilistic noise. The proposed change detection algorithm is applied to oneand two-dimensional patterns to detect the abrupt changes.

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Section: A Brief Overview Of the Methodologies Used To Datementioning

confidence: 99%

A New Approach to Detection of Changes in Multidimensional Patterns

Gałkowski

Krzyżak

Filutowicz

2020

Journal of Artificial Intelligence and Soft Computing Research

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“…Their membership degree is represented by an interval in place of a fuzzy set. [65] The fundamental development and theoretical progress in interval type-2 fuzzy systems and type-2 fuzzy systems are discussed by Mendel [66]. In the interval type-2 fuzzy sets, the computation of centroids is an important operation that is a very timeconsuming task.…”

Section: The Interpretability and Accuracy Issues About Fuzzy Systemsmentioning

confidence: 99%

A Review on the Development of Fuzzy Classifiers with Improved Interpretability and Accuracy Parameters

Azad¹,

Shukla²

2021

J. Infor. Electr. Electron. Eng.

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This review paper of fuzzy classifiers with improved interpretability and accuracy parameter discussed the most fundamental aspect of very effective and powerful tools in form of probabilistic reasoning, The fuzzy logic concept allows the effective realization of ap-proximate, vague, uncertain, dynamic, and more realistic conditions, which is closer to the actual physical world and human thinking. The fuzzy theory has the competency to catch the lack of preciseness of linguistic terms in a speech of natural language. The fuzzy theory provides a more significant competency to model humans like com-mon-sense reasoning and conclusion making to fuzzy set and rules as good membership function. Also, this paper reviews discussed the evaluation of the fuzzy set, type-1, type-2, and interval type-2 fuzzy system from traditional Boolean crisp set logic along with interpretability and accuracy issues in the fuzzy system.

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“…More and more studies begin to focus on how to improve the interpretability of recommendations [27] [21]. The important idea is to use auxiliary information to improve interpretability, such as user reviews and item descriptions.…”

Section: Introductionmentioning

confidence: 99%

TextOG: A Recommendation Model for Rating Prediction Based on Heterogeneous Fusion of Review Data

Yang

Zhang

2020

IEEE Access

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It is beneficial to use user review as a preference expression because they contain information that is not in the interaction record. However, most current research on recommendation systems only models the explicit records of users and items. It does not mine more personalized information from the review texts generated simultaneously with the interaction records. In this article, we proposed a heterogeneous fusion recommendation model for extracting fine-grained product attributes and user behavior from the review texts. The model we proposed is called TextOG. In the first half of the model, we used two blocks to learn user reviews and item reviews, one of which is dedicated to learning user behavior using reviews written by users, and the other block determines product attributes from reviews written for products. In the second half of the model, we connected the latent factors learned by users and items to perform spatial convolution on the graph. That way, implicit features can perform complex interactions in non-Euclidean spaces. We conducted experiments on a large review data set, and the results show that TextOG performs better than the baseline recommendation methods on various datasets.

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Towards Interpretability of the Movie Recommender Based on a Neuro-Fuzzy Approach

Cited by 11 publications

References 21 publications

A New Approach to Detection of Changes in Multidimensional Patterns

A New Approach to Detection of Changes in Multidimensional Patterns

A Review on the Development of Fuzzy Classifiers with Improved Interpretability and Accuracy Parameters

TextOG: A Recommendation Model for Rating Prediction Based on Heterogeneous Fusion of Review Data

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