Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40078
Title: Making better recommendations with online profiling agents
Authors: Oh, D.C.H.
Tan, C.L. 
Issue Date: 2005
Source: Oh, D.C.H.,Tan, C.L. (2005). Making better recommendations with online profiling agents. AI Magazine 26 (3) : 29-39. ScholarBank@NUS Repository.
Abstract: In recent years, we have witnessed the success of autonomous agents applying machine-learning techniques across a wide range of applications. However, agents applying the same machine-learning techniques in online applications have not been so successful. Even agent-based hybrid recommender systems that combine information filtering techniques with collaborative filtering techniques have been applied with considerable success only to simple consumer goods such as movies, books, clothing, and food. Yet complex, adaptive autonomous agent systems that can handle complex goods such as real estate, vacation plans, insurance, mutual funds, and mortgages have emerged. To a large extent, the reinforcement learning methods developed to aid agents in learning have been more successfully deployed in offline applications. The inherent limitations in these methods have rendered them somewhat ineffective in online applications. In this article, we postulate that a small amount of prior knowledge and human-provided input can dramatically speed up online learning. We demonstrate that our agent HumanE - with its prior knowledge or "experiences" about the real estate domain - can effectively assist users in identifying requirements, especially unstated ones, quickly and unobtrusively. Copyright © 2005, American Association for Artificial Intelligence. All rights reserved.
Source Title: AI Magazine
URI: http://scholarbank.nus.edu.sg/handle/10635/40078
ISSN: 07384602
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

65
checked on Dec 9, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.