scholarly journals WEB RECOMMENDER SYSTEM BASED ON CONSUMER BEHAVIOR MODELING USING FUZZY REPRESENTATION

2015 ◽  
Vol 2 (06) ◽  
Keyword(s):  
Consumer Behavior ◽  
Recommender System ◽  
Behavior Modeling ◽  
Fuzzy Representation

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

scholarly journals AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System

2021 ◽  
Author(s):  
Pengyu Zhao ◽  
Kecheng Xiao ◽  
Yuanxing Zhang ◽  
Kaigui Bian ◽  
Wei Yan
Keyword(s):  
Recommender System ◽  
Random Search ◽  
Search Space ◽  
Behavior Modeling ◽  
Model Complexity ◽  
Domain Experts ◽  
Neural Architecture ◽  
Automatic Behavior ◽  
Three Stages ◽  
The One

Recently, deep learning models have been widely explored in recommender systems. Though having achieved remarkable success, the design of task-aware recommendation models usually requires manual feature engineering and architecture engineering from domain experts. To relieve those efforts, we explore the potential of neural architecture search (NAS) and introduce AMEIR for Automatic behavior Modeling, interaction Exploration and multi-layer perceptron (MLP) Investigation in the Recommender system. Specifically, AMEIR divides the complete recommendation models into three stages of behavior modeling, interaction exploration, MLP aggregation, and introduces a novel search space containing three tailored subspaces that cover most of the existing methods and thus allow for searching better models. To find the ideal architecture efficiently and effectively, AMEIR realizes the one-shot random search in recommendation progressively on the three stages and assembles the search results as the final outcome. The experiment over various scenarios reveals that AMEIR outperforms competitive baselines of elaborate manual design and leading algorithmic complex NAS methods with lower model complexity and comparable time cost, indicating efficacy, efficiency, and robustness of the proposed method.

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