scholarly journals LAR: A User Behavior Prediction Model in Server Log Based on LSTM-Attention Network and RSC Algorithm

2020 ◽  
Author(s):  
Yingying Shang
Keyword(s):  
Clustering Algorithm ◽  
Short Term Memory ◽  
User Behavior ◽  
Absolute Error ◽  
Research Area ◽  
Behavior Prediction ◽  
Attention Network ◽  
Important Research Area ◽  
User Access ◽  
Single User

Using server log data to predict the URLs that a user is likely to visit is an important research area in user behavior prediction. In this paper, a predictive model (called LAR) based on the long short-term memory (LSTM) attention network and reciprocal-nearest-neighbors supported clustering algorithm (RSC) for predicting the URL is proposed. First, the LSTM-attention network is used to predict the URL categories a user might visit, and the RSC algorithm is then used to cluster users. Subsequently, the URLs belonging to the same category are determined from the user clusters to predict the URLs that the user might visit. The proposed LAR model considers the time sequence of the user access URL, and the relationship between a single user and group users, which effectively improves the prediction accuracy. The experimental results demonstrate that the LAR model is feasible and effective for user behavior prediction. The accuracy of the mean absolute error and root mean square error of the LAR model are better than those of the other models compared in this study.

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).

User Behavior Prediction Analysis Based on Time Context

2014 ◽  
Vol 989-994 ◽  
pp. 4920-4925
Author(s):  
Wei Gu ◽  
Lian Jun Chen
Keyword(s):  
Behavior Management ◽  
Clustering Algorithm ◽  
User Behavior ◽  
Business Value ◽  
Management Control ◽  
Behavior Prediction ◽  
Prediction Analysis ◽  
Different Types ◽  
Important Significance

Analysis of user behavior management control has important significance for network users. This paper uses the clustering algorithm to cluster the user behavior for clicking certain software or Website in different times, through the time context of user behavior to analyze user behavior rules. Additionally, this paper analyzes the clustering result in detail, then, divides user behavior into different types. Finally, according to the clustering result, more targeted pages and applications are recommended to the network users to create huge business value.

scholarly journals Attention-Based LSTM with Filter Mechanism for Entity Relation Classification

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1729 ◽  
Author(s):  
Yanliang Jin ◽  
Dijia Wu ◽  
Weisi Guo
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Research Area ◽  
Attention Mechanism ◽  
Key Word ◽  
Previous State ◽  
Important Research Area ◽  
Lstm Network ◽  
Relation Classification ◽  
Filter Mechanism

Relation classification is an important research area in the field of natural language processing (NLP), which aims to recognize the relationship between two tagged entities in a sentence. The noise caused by irrelevant words and the word distance between the tagged entities may affect the relation classification accuracy. In this paper, we present a novel model multi-head attention long short term memory (LSTM) network with filter mechanism (MALNet) to extract the text features and classify the relation of two entities in a sentence. In particular, we combine LSTM with attention mechanism to obtain the shallow local information and introduce a filter layer based on attention mechanism to strength the available information. Besides, we design a semantic rule for marking the key word between the target words and construct a key word layer to extract its semantic information. We evaluated the performance of our model on SemEval-2010 Task8 dataset and KBP-37 dataset. We achieved an F1-score of 86.3% on SemEval-2010 Task8 dataset and F1-score of 61.4% on KBP-37 dataset, which shows that our method is superior to the previous state-of-the-art methods.

Research Commentary: Analysis of Curriculum Standards: An Important Research Area

2016 ◽  
Vol 47 (2) ◽  
pp. 118-133 ◽  
Author(s):  
Dung Tran ◽  
Barbara J. Reys ◽  
Dawn Teuscher ◽  
Shannon Dingman ◽  
Lisa Kasmer
Keyword(s):  
Mathematics Education ◽  
Education Reform ◽  
Careful Analysis ◽  
Research Area ◽  
State Standards ◽  
Content Standards ◽  
Curriculum Standards ◽  
Education Professionals ◽  
Important Research Area ◽  
Improving Student Learning

This commentary highlights the contribution that careful and systematic analyses of curriculum or content standards can make to questions and issues important in the mathematics education field. We note the increased role that curriculum standards have played as part of a standards-based education reform strategy. We also review different methods used by researchers to compare and analyze the Common Core State Standards for Mathematics, each method designed for a particular purpose. Finally, we call upon mathematics education researchers to engage in careful analysis of curriculum standards and to share their findings in ways that can inform public debate as well as support education professionals in improving student learning opportunities.

scholarly journals On the Analysis of PM/FM Noise Radar Waveforms Considering Modulating Signals with Varied Stochastic Properties

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1727
Author(s):  
Leandro Pralon ◽  
Gabriel Beltrao ◽  
Alisson Barreto ◽  
Bruno Cosenza
Keyword(s):  
Random Phase ◽  
Research Area ◽  
General Term ◽  
Radar Systems ◽  
Noise Radar ◽  
Random Samples ◽  
Spectrum Density ◽  
Important Research Area ◽  
Radar Waveforms ◽  
Stochastic Properties

Noise Radar technology is the general term used to describe radar systems that employ realizations of a given stochastic process as transmit waveforms. Originally, carriers modulated in amplitude by a Gaussian random signal, derived from a hardware noise source, were taken into consideration, justifying the adopted nomenclature. With the advances made in hardware as well as the rise of the software defined noise radar concept, waveform design emerges as an important research area related to such systems. The possibility of generating signals with varied stochastic properties increased the potential in achieving systems with enhanced performances. The characterization of random phase and frequency modulated waveforms (more suitable for several applications) has then gained considerable notoriety within the radar community as well. Several optimization algorithms have been proposed in order to conveniently shape both the autocorrelation function of the random samples that comprise the transmit signal, as well as their power spectrum density. Nevertheless, little attention has been driven to properly characterize the stochastic properties of those signals through closed form expressions, jeopardizing the effectiveness of the aforementioned algorithms as well as their reproducibility. Within this context, this paper investigates the performance of several random phase and frequency modulated waveforms, varying the stochastic properties of their modulating signals.

Data-driven User Behavior Prediction Based on Integrated Prediction Model

2021 ◽  
Author(s):  
Xiangyu Zhang ◽  
Jun Fang ◽  
Jingfan Zou ◽  
Wenfang Li ◽  
Weigang Xu ◽  
...  
Keyword(s):  
Prediction Model ◽  
User Behavior ◽  
Data Driven ◽  
Behavior Prediction

Biomedical Document Clustering Based on Accelerated Symbiotic Organisms Search Algorithm

2021 ◽  
Vol 12 (4) ◽  
pp. 169-185
Author(s):  
Saida Ishak Boushaki ◽  
Omar Bendjeghaba ◽  
Nadjet Kamel
Keyword(s):  
Clustering Algorithm ◽  
Search Algorithm ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Latent Semantic Indexing ◽  
Research Area ◽  
Semantic Indexing ◽  
Local Optima ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

Clustering is an important unsupervised analysis technique for big data mining. It finds its application in several domains including biomedical documents of the MEDLINE database. Document clustering algorithms based on metaheuristics is an active research area. However, these algorithms suffer from the problems of getting trapped in local optima, need many parameters to adjust, and the documents should be indexed by a high dimensionality matrix using the traditional vector space model. In order to overcome these limitations, in this paper a new documents clustering algorithm (ASOS-LSI) with no parameters is proposed. It is based on the recent symbiotic organisms search metaheuristic (SOS) and enhanced by an acceleration technique. Furthermore, the documents are represented by semantic indexing based on the famous latent semantic indexing (LSI). Conducted experiments on well-known biomedical documents datasets show the significant superiority of ASOS-LSI over five famous algorithms in terms of compactness, f-measure, purity, misclassified documents, entropy, and runtime.

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