scholarly journals An Unsupervised User Behavior Prediction Algorithm Based on Machine Learning and Neural Network For Smart Home

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 49237-49247 ◽  
Author(s):  
Tiankai Liang ◽  
Bi Zeng ◽  
Jianqi Liu ◽  
Linfeng Ye ◽  
Caifeng Zou
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Smart Home ◽  
User Behavior ◽  
Prediction Algorithm ◽  
Behavior Prediction

Cooking Procedure Recognition and Support by Ubiquitous Sensors

2009 ◽  
Vol 21 (4) ◽  
pp. 498-506 ◽  
Author(s):  
Sho Murakami ◽  
◽  
Takuo Suzuki ◽  
Akira Tokumasu ◽  
Yasushi Nakauchi
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
User Behavior ◽  
Learning Algorithm ◽  
Experimental Results ◽  
Prediction Algorithm ◽  
Machine Learning Algorithm ◽  
Behavior Prediction ◽  
Model Based ◽  
Sensor Information

This paper proposes cooking support using ubiquitous sensors. We developed a machine learning algorithm that recognizes cooking procedures by taking into account widely varying sensor information and user behavior. To provide appropriate instructions to users, we developed a Markov-model-based behavior prediction algorithm. Using these algorithms, we developed cooking support automatically displaying cooking instruction videos based on user progress. Experiments and experimental results confirmed the feasibility of our proposed cooking support.

Ensemble machine learning-based algorithm for electric vehicle user behavior prediction

2019 ◽  
Vol 254 ◽  
pp. 113732 ◽  
Author(s):  
Yu-Wei Chung ◽  
Behnam Khaki ◽  
Tianyi Li ◽  
Chicheng Chu ◽  
Rajit Gadh
Keyword(s):  
Machine Learning ◽  
Electric Vehicle ◽  
User Behavior ◽  
Behavior Prediction ◽  
Ensemble Machine Learning

scholarly journals Graph Neural Network and Context-Aware Based User Behavior Prediction and Recommendation System Research

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Qian Gao ◽  
Pengcheng Ma
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
User Behavior ◽  
Absolute Error ◽  
User Preferences ◽  
Feature Interaction ◽  
Context Aware ◽  
Behavior Prediction ◽  
Complex Interactions ◽  
System Maps

Due to the influence of context information on user behavior, context-aware recommendation system (CARS) has attracted extensive attention in recent years. The most advanced context-aware recommendation system maps the original multi-field features into a shared hidden space and then simply connects it to a deep neural network (DNN) or other specially designed networks. However, for different areas, the ability of modeling complex interactions in a sufficiently flexible and explicit way is limited by the simple unstructured combination of feature fields. Therefore, it is hard to get the accurate results of the user behavior prediction. In this paper, a graph structure is used to establish the interaction between context and users/items. Through modeling user behavior, we can explore user preferences in different context environments, so as to make personalized recommendations for users. In particular, we construct a context-user and context-item interactions graph separately. In the interactions graph, each node is composed of a user feature field, an item feature field, and a feature field of different contexts. Different feature fields can interact through edges. Therefore, the task of modeling feature interaction can be transformed into modeling the node interaction on the corresponding graph. To this end, an innovative model called context-aware graph neural network (CA-GNN) model is designed. Furthermore, in order to obtain more accurate and efficient recommendation results, first, we innovatively use the attention mechanism to improve the interpretability of CA-GNN; second, we innovatively use the degree of physical fatigue features which has never been used in traditional CARS as critical contextual feature information into our CA-GNN. We simulated the Food and Yelp datasets. The experimental results show that CA-GNN is better than other methods in terms of root mean square error (RMSE) and mean absolute error (MAE).

Predictive analysis of user behavior of E-commerce platform based on machine learning image algorithm in internet of things environment

2021 ◽  
pp. 1-8
Author(s):  
Jiacheng Ni
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Bp Neural Network ◽  
Theoretical Foundation ◽  
User Behavior ◽  
Rbf Neural Network ◽  
Model Structure ◽  
Internet Of Things Environment ◽  
Detailed Method ◽  
Overall Performance

With the development of the Internet, the rise of e-commerce has changed the shopping habits of most people. The research of this article is mainly divided into three parts. The first part is the theoretical foundation and core concept research. The second part of this article is a detailed method of establishing a predictive model based on machine learning image algorithms. In addition to reclassifying features, image algorithms are also used to optimize the model structure. The third part is the experimental results and analysis. After comparing with BP neural network and RBF neural network, through data analysis, the prediction model in this paper greatly improves the prediction accuracy and time, and the overall performance has a breakthrough.

A user behavior prediction model based on parallel neural network and k-nearest neighbor algorithms

2017 ◽  
Vol 20 (2) ◽  
pp. 1703-1715 ◽  
Author(s):  
Gaowei Xu ◽  
Carl Shen ◽  
Min Liu ◽  
Feng Zhang ◽  
Weiming Shen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Nearest Neighbor ◽  
User Behavior ◽  
Behavior Prediction ◽  
K Nearest Neighbor ◽  
Model Based
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