Effective Web content recommendation based on consumer behavior modeling

2011 ◽  
Author(s):  
Baoyao Zhou ◽  
A. C. M. Fong ◽  
Siu C. Hui ◽  
The A. Do
Keyword(s):  
Consumer Behavior ◽  
Behavior Modeling ◽  
Web Content ◽  
Content Recommendation

Web content recommender system based on consumer behavior modeling

2011 ◽  
Vol 57 (2) ◽  
pp. 962-969 ◽  
Author(s):  
A. Fong ◽  
Baoyao Zhou ◽  
S. Hui ◽  
Guan Hong ◽  
The Do
Keyword(s):  
Consumer Behavior ◽  
Recommender System ◽  
Behavior Modeling ◽  
Web Content

Patterns of people's financial and consumer behavior due to the COVID-19 pandemic as a trigger of the macroeconomic destabilization

2021 ◽  
Vol 27 (2) ◽  
pp. 417-429
Author(s):  
Denis Yu. RAZUMOVSKII
Keyword(s):  
Consumer Behavior ◽  
Financial Literacy ◽  
Task Force ◽  
Behavioral Pattern ◽  
Behavior Modeling ◽  
Stress Factors ◽  
Financial Behavior ◽  
State University ◽  
Financial Decisions ◽  
The Impact

Subject. The article discusses the financial behavior of households, and financial decisions. Objectives. I model patterns of households' financial behavior, referring the impact of stress factors on financial decisions. Methods. The financial behavior was analyzed through approaches proposed by D. Kahneman, A. Tversky and R. Thaler. However, instead of experiments, I rely upon surveys evaluating the financial literacy and behavior of people in the Sverdlovsk Oblast via social networks and conduct my research as a member of the task force of the Regions Center for Financial Literacy at the Ural State University of Economics. I took part in the preparation of questionnaires. Results. I proposed model patterns of financial behavior and substantiate what determines the behavioral pattern of people in distress. I also conclude that the impact of the COVID-19 on the financial and consumer behavior of people triggers destabilizing effects for the macroeconomic situation. Conclusions. Financial behavior modeling will help forecast financial and consumer shocks when the macroeconomic situation is destabilized, thus transforming the social policy.

scholarly journals Consumer Behavior Modeling: Fuzzy Logic Model for Air Purifiers Choosing

2017 ◽  
Vol 13 (4) ◽  
pp. 61-77 ◽  
Author(s):  
Oleksandr Dorokhov ◽  
Liudmyla Dorokhova ◽  
Milica Delibasic ◽  
Justas Streimikis
Keyword(s):  
Fuzzy Logic ◽  
Consumer Behavior ◽  
Logic Model ◽  
Behavior Modeling ◽  
Fuzzy Logic Model

Web Content Recommendation Methods Based on Reinforcement Learning

2011 ◽  
pp. 2353-2380
Author(s):  
Nima Taghipour ◽  
Ahmad Kardan
Keyword(s):  
Reinforcement Learning ◽  
Recommender Systems ◽  
Information Overload ◽  
User Behavior ◽  
Web Content ◽  
Web Usage ◽  
Content Recommendation ◽  
Conceptual Relationships ◽  
The Web

Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests. In this chapter we introduce our novel machine learning perspective toward the web recommendation problem, based on reinforcement learning. Our recommendation method makes use of the web usage and content data to learn a predictive model of users’ behavior on the web and exploits the learned model to make web page recommendations. Unlike other recommender systems, our system does not use the static patterns discovered from web usage data, instead it learns to make recommendations as the actions it performs in each situation. In the proposed method we combined the conceptual and usage information in order to gain a more general model of user behavior and improve the quality of web recommendations. A hybrid web recommendation method is proposed by making use of the conceptual relationships among web resources to derive a novel model of the problem, enriched with semantic knowledge about the usage behavior. The method is evaluated under different settings and it is shown how this method can improve the overall quality of recommendations.

Web Content Recommendation Methods Based on Reinforcement Learning

2010 ◽  
pp. 222-249
Author(s):  
Nima Taghipour ◽  
Ahmad Kardan
Keyword(s):  
Reinforcement Learning ◽  
Recommender Systems ◽  
Information Overload ◽  
User Behavior ◽  
Web Content ◽  
Web Usage ◽  
Content Recommendation ◽  
Conceptual Relationships ◽  
The Web

Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests. In this chapter the authors introduce their novel machine learning perspective toward the Web recommendation problem, based on reinforcement learning. Our recommendation method makes use of the Web usage and content data to learn a predictive model of users’ behavior on the Web and exploits the learned model to make Web page recommendations. Unlike other recommender systems, this system does not use the static patterns discovered from Web usage data, instead it learns to make recommendations as the actions it performs in each situation. In the proposed method the authors combined the conceptual and usage information in order to gain a more general model of user behavior and improve the quality of web recommendations. A hybrid Web recommendation method is proposed by making use of the conceptual relationships among Web resources to derive a novel model of the problem, enriched with semantic knowledge about the usage behavior. The method is evaluated under different settings and it is shown how this method can improve the overall quality of recommendations.

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