Intelligent User Preference Detection for Product Brokering

Intelligent Personalization Agent for Product Brokering

Encyclopedia of Multimedia Technology and Networking, Second Edition ◽

10.4018/978-1-60566-014-1.ch095 ◽

2009 ◽

pp. 703-709

Author(s):

Sheng-Uei Guan

Keyword(s):

Complex Analysis ◽

Software Agents ◽

User Preference ◽

General Description ◽

Product Attributes ◽

Intelligent Software ◽

Intelligent Software Agents ◽

Business To Consumer ◽

Intelligent Filtering ◽

Description Field

A good business to consumer environment can be developed through the creation of intelligent software agents (Guan, Zhi, & Maung, 2004; Soltysiak & Crabtree, 1998) to fulfill the needs of consumers patronizing online e-commerce stores (Guan, 2006). This includes intelligent filtering services (Chanan, 2001) and product brokering services (Guan, Ngoo, & Zhu, 2002) to understand a user’s needs before alerting the user of suitable products according to his needs and preference. We present an approach to capture user response toward product attributes, including nonquantifiable ones. The proposed solution does not generalize or stereotype user preference but captures the user’s unique taste and recommends a list of products to the user. Under the proposed approach, the system is able to handle the inclusion of any unaccounted attribute that is not predefined in the system, without reprogramming the system. The system is able to cater to any unaccounted attribute through a general description field found in most product databases. This is useful, as hundreds of new attributes of products emerge each day, making any complex analysis impossible. In addition, the system is selfadjusting in nature and can adapt to changes in user preference.

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Intelligent User Preference Mining

Encyclopedia of Information Communication Technology ◽

10.4018/978-1-59904-845-1.ch062 ◽

2009 ◽

pp. 470-476

Author(s):

Sheng-Uei Guan ◽

Ping Cheng Tan

Keyword(s):

Software Agents ◽

User Preference ◽

Product Attributes ◽

Individual User ◽

Specific Preference ◽

Business To Consumer ◽

Intelligent Filtering

A business-to-consumer environment can be developed through software agents (Guan, Zhu, & Maung, 2004; Maes, 1994; Nwana & Ndumu, 1996; Wang, Guan, & Chan, 2002) to satisfy the needs of consumers patronizing online e-commerce or m-commerce stores. This includes intelligent filtering services (Chanan & Yadav, 2000) and product brokering services to understand user’s needs better before alerting users of suitable products according to their preference. We present an approach to capture individual user response towards product attributes including nonquantifiable responses. The proposed solution can capture the user’s specific preference and recommend a list of products from the product database. With the proposed approach, the system can handle any unaccounted attribute that is undefined in the system. The system is able to cater to any unaccounted attribute through a general descriptions field found in most product databases. In addition, the system can adapt to changes in user’s preference.

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Intelligent User Preference Mining

Software Applications ◽

10.4018/978-1-60566-060-8.ch033 ◽

2009 ◽

pp. 486-494

Author(s):

Sheng-Uei Guan ◽

Ping Cheng Tan

Keyword(s):

Software Agents ◽

User Preference ◽

Product Attributes ◽

Individual User ◽

Specific Preference ◽

Business To Consumer ◽

Intelligent Filtering

A business-to-consumer environment can be developed through software agents (Guan, Zhu, & Maung, 2004; Maes, 1994; Nwana & Ndumu, 1996; Wang, Guan, & Chan, 2002) to satisfy the needs of consumers patronizing online e-commerce or m-commerce stores. This includes intelligent filtering services (Chanan & Yadav, 2000) and product brokering services to understand user’s needs better before alerting users of suitable products according to their preference. We present an approach to capture individual user response towards product attributes including nonquantifiable responses. The proposed solution can capture the user’s specific preference and recommend a list of products from the product database. With the proposed approach, the system can handle any unaccounted attribute that is undefined in the system. The system is able to cater to any unaccounted attribute through a general descriptions field found in most product databases. In addition, the system can adapt to changes in user’s preference.

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Application of Fuzzy Logic for Adaptive Food Recommendation

International Journal of Fuzzy System Applications ◽

10.4018/ijfsa.2017040106 ◽

2017 ◽

Vol 6 (2) ◽

pp. 110-133 ◽

Cited By ~ 1

Author(s):

Tousif Osman ◽

Maisha Mahjabeen ◽

Shahreen Shahjahan Psyche ◽

Afsana Imam Urmi ◽

J.M. Shafi Ferdous ◽

...

Keyword(s):

Artificial Intelligence ◽

Fuzzy Logic ◽

User Profile ◽

Feedback System ◽

User Feedback ◽

User Preference ◽

Individual User ◽

Food Items ◽

Fuzzy Database

The research introduces an adaptive food searching and recommending engine by taste and user preference using fuzzy logic. In contrast with existing system where food is searched by predefined keywords, this system searches food by its taste and users' preference which allows the system to provide better results. As food taste cannot be measured and user's preference is relative to each user, the authors have used concepts of artificial intelligence (AI) and fuzzy logic to better understand and deal the abstractness of these parameters. Along with food taste the authors have considered restaurant's environment, location, review and user's budget as searching parameters. The system includes a fuzzy database where food items of different restaurants with the specific parameters have been stored and gets updated by user feedback. System also maintains a user profile for individual user to adapt with individual user's choice of preference.

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