Автор: Tarnowska Katarzyna, Ras Zbigniew W., Daniel Lynn Название: Recommender System for Improving Customer Loyalty ISBN: 3030134407 ISBN-13(EAN): 9783030134402 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the Recommender System for Improving Customer Loyalty. The data mining techniques employed in the Recommender System allow users to "learn" from the experiences of others, without sharing proprietary information.
Автор: Katarzyna Tarnowska; Zbigniew W. Ras; Lynn Daniel Название: Recommender System for Improving Customer Loyalty ISBN: 3030134377 ISBN-13(EAN): 9783030134372 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the Recommender System for Improving Customer Loyalty. New and innovative products have begun appearing from a wide variety of countries, which has increased the need to improve the customer experience. When a customer spends hundreds of thousands of dollars on a piece of equipment, keeping it running efficiently is critical to achieving the desired return on investment. Moreover, managers have discovered that delivering a better customer experience pays off in a number of ways. A study of publicly traded companies conducted by Watermark Consulting found that from 2007 to 2013, companies with a better customer service generated a total return to shareholders that was 26 points higher than the S&P 500. This is only one of many studies that illustrate the measurable value of providing a better service experience.
The Recommender System presented here addresses several important issues. (1) It provides a decision framework to help managers determine which actions are likely to have the greatest impact on the Net Promoter Score. (2) The results are based on multiple clients. The data mining techniques employed in the Recommender System allow users to “learn” from the experiences of others, without sharing proprietary information. This dramatically enhances the power of the system. (3) It supplements traditional text mining options. Text mining can be used to identify the frequency with which topics are mentioned, and the sentiment associated with a given topic. The Recommender System allows users to view specific, anonymous comments associated with actual customers. Studying these comments can provide highly accurate insights into the steps that can be taken to improve the customer experience. (4) Lastly, the system provides a sensitivity analysis feature. In some cases, certain actions can be more easily implemented than others. The Recommender System allows managers to “weigh” these actions and determine which ones would have a greater impact.
Автор: Aggarwal Charu C. Название: Recommender Systems: The Textbook ISBN: 331980619X ISBN-13(EAN): 9783319806198 Издательство: Springer Рейтинг: Цена: 9362.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.
Автор: Fatih Gedikli Название: Recommender Systems and the Social Web ISBN: 3658019476 ISBN-13(EAN): 9783658019471 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: ГЇВїВЅ There is an increasing demand for recommender systems due to the information overload users are facing on the Web. A tag recommender algorithm is proposed which recommends tags for users to annotate their favorite online resources.
Автор: Charu C. Aggarwal Название: Recommender Systems ISBN: 3319296574 ISBN-13(EAN): 9783319296579 Издательство: Springer Рейтинг: Цена: 9362.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.
Автор: Cai-Nicolas Ziegler Название: Social Web Artifacts for Boosting Recommenders ISBN: 331900526X ISBN-13(EAN): 9783319005263 Издательство: Springer Рейтинг: Цена: 19591.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents approaches for exploiting the rapidly expanding fountain of Social Web knowledge by means of classification taxonomies and trust networks, which are used to improve the performance of product-focused recommender systems.
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