UID:
almahu_9949083915202882
Format:
XIX, 167 p. 118 illus., 72 illus. in color.
,
online resource.
Edition:
1st ed. 2021.
ISBN:
9783030755218
Series Statement:
Studies in Computational Intelligence, 964
Content:
The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.
Note:
Introduction -- Neuro-Fuzzy Approach and its Application in Recommender Systems -- Novel Explainable Recommenders Based on Neuro-Fuzzy -- Explainable Recommender for Investment Advisers -- Summary and Final Remarks.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030755201
Additional Edition:
Printed edition: ISBN 9783030755225
Additional Edition:
Printed edition: ISBN 9783030755232
Language:
English
Subjects:
Computer Science
DOI:
10.1007/978-3-030-75521-8
URL:
https://doi.org/10.1007/978-3-030-75521-8
URL:
Volltext
(URL des Erstveröffentlichers)
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