Explainable Recommendation: A Survey and New Perspectives (Foundations and TrendsŪ in Information Retrieval)

Yongfeng Zhang (author) & Xu Chen (author)

ISBN 10: 1680836587 / ISBN 13: 9781680836585

Published by Now Publishers -, 2020

About this Item

Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.

Seller Inventory # 6545-9781680836585

Bibliographic Details

• Title: Explainable Recommendation: A Survey and New Perspectives (Foundations and TrendsŪ in Information Retrieval)
• Publisher: Now Publishers -
• Publication Date: 2020
• Language: English
• ISBN 10: 1680836587
• ISBN 13: 9781680836585
• Binding: paperback
• Condition: Very Good

About this title

Synopsis

Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. It tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain items are recommended by the algorithm, where the human can either be users or system designers. Explainable recommendation helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction of recommendation systems, and facilitates system designers for better system debugging. In recent years, a large number of explainable recommendation approaches have been proposed and applied in real-world systems. This survey provides a comprehensive review of the explainable recommendation research. The authors first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W (what, when, who, where, and why). They then conduct a comprehensive survey of explainable recommendation on three perspectives: (1) a chronological research timeline of explainable recommendation; (2) a two-dimensional taxonomy to classify existing explainable recommendation research; (3) a summary of how explainable recommendation applies to different recommendation tasks. The authors also devote a section to discuss the explanation perspectives in broader IR and AI/ML research and end the survey by discussing potential future directions to promote the explainable recommendation research area and beyond.