Normal View MARC View ISBD View

Persuasive Recommender Systems

by Yoo, Kyung-Hyan.
Authors: Gretzel, Ulrike.%author. | Zanker, Markus.%author. | SpringerLink (Online service) Series: SpringerBriefs in Electrical and Computer Engineering, 2191-8112 Physical details: VI, 59 p. 9 illus. online resource. ISBN: 146144702X Subject(s): Computer science. | Data mining. | Artificial intelligence. | Computer Science. | Artificial Intelligence (incl. Robotics). | Data Mining and Knowledge Discovery.
Tags from this library:
No tags from this library for this title.
Item type Location Call Number Status Date Due
E-Book E-Book AUM Main Library 006.3 (Browse Shelf) Not for loan

Introduction -- Theoretical Background -- Source Factors -- Message Factors -- Receiver and Context Factors -- Discussion -- Implications for Recommender System Design -- Directions for future research.

Whether users are likely to accept the recommendations provided by a recommender system is of utmost importance to system designers and the marketers who implement them. By conceptualizing the advice seeking and giving relationship as a fundamentally social process, important avenues for understanding the persuasiveness of recommender systems open up. Specifically, research regarding influential factors in advice seeking relationships, which is abundant in the context of human-human relationships, can provide an important framework for identifying potential influence factors in recommender system context. This book reviews the existing literature on the factors in advice seeking relationships in the context of human-human, human-computer, and human-recommender system interactions. It concludes that many social cues that have been identified as influential in other contexts have yet to be implemented and tested with respect to recommender systems. Implications for recommender system research and design are discussed.

There are no comments for this item.

Log in to your account to post a comment.