scholarly journals Performance of Combined Recommendation Modeling Fused With Tagawareness In Mixed Traffic Scenarios

2021 ◽  
Author(s):  
Xiangyuan Li ◽  
Fei Ding ◽  
Suju Ren ◽  
Jianmin Bao ◽  
Ruoyu Su ◽  
...  
Keyword(s):  
High Speed ◽  
Recommendation System ◽  
Short Term Memory ◽  
User Behavior ◽  
Data Sets ◽  
Mixed Traffic ◽  
User Interest ◽  
Travel Information ◽  
Lstm Network ◽  
Behavior Characteristics

Abstract Due to the heterogeneous characteristics of vehicles and user terminals, information in mixed traffic scenarios can be interacted based on the Web protocol of different terminals. The recommendation system can dig users' travel preferences by analyzing historical travel information of different traffic participants, to publish accurate travel information and services for the terminals of traffic participants. The diversification of existing road network users and networking modes, as well as the dynamic changes of user interest distribution caused by high-speed movement of vehicles, traditional collaborative filtering algorithms have limitations in terms of effectiveness. This paper proposes a novel Hybrid Tag-aware Recommender Model (HTRM). The model embedding layer first employs the Word2vec model to represent the tags and ratings of projects and users, respectively. The feature layer then introduces the auto-encoder to extract self-similar features of the item, and a long short-term memory (LSTM) network is used to extract user behavior characteristics to provide higher-quality recommendations. The gating layer combines the features of users and projects and then makes score recommendations based on the Fully Connected Neural Network (FCNN). Finally, Web data sets of different service preferences of traffic participants during the trip are used to evaluate the model recommendation performance in different scenarios. The experimental results show that the HTRM model is reasonable in design and can achieve high recommendation accuracy.

scholarly journals A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model

2020 ◽  
Vol 12 (10) ◽  
pp. 4107
Author(s):  
Wafa Shafqat ◽  
Yung-Cheol Byun
Keyword(s):  
Recommendation System ◽  
Short Term Memory ◽  
Contextual Information ◽  
Optimal Size ◽  
Effective Parameters ◽  
Travel History ◽  
Proposed Model ◽  
Processing Information ◽  
Lstm Network ◽  
Gated Recurrent Unit

The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term memory (LSTM) based context-enriched hierarchical model. This proposed model had two levels of hierarchy and each level comprised of a deep LSTM network. In each level, the task of the LSTM was different. At the first level, LSTM learned from user travel history and predicted the next location probabilities. A contextual learning unit was active between these two levels. This unit extracted maximum possible contexts related to a location, the user and its environment such as weather, climate and risks. This unit also estimated other effective parameters such as the popularity of a location. To avoid feature congestion, XGBoost was used to rank feature importance. The features with no importance were discarded. At the second level, another LSTM framework was used to learn these contextual features embedded with location probabilities and resulted into top ranked places. The performance of the proposed approach was elevated with an accuracy of 97.2%, followed by gated recurrent unit (GRU) (96.4%) and then Bidirectional LSTM (94.2%). We also performed experiments to find the optimal size of travel history for effective recommendations.

LSTM-based soft sensor design for oxygen content of flue gas in coal-fired power plant

2020 ◽  
pp. 014233122093239
Author(s):  
Hongguang Pan ◽  
Tao Su ◽  
Xiangdong Huang ◽  
Zheng Wang
Keyword(s):  
Power Plant ◽  
Oxygen Content ◽  
Flue Gas ◽  
Oxygen Sensor ◽  
Short Term Memory ◽  
Support Vector ◽  
Data Sets ◽  
Auxiliary Variables ◽  
Data Set ◽  
Lstm Network

To address problems of high cost, complicated process and low accuracy of oxygen content measurement in flue gas of coal-fired power plant, a method based on long short-term memory (LSTM) network is proposed in this paper to replace oxygen sensor to estimate oxygen content in flue gas of boilers. Specifically, first, the LSTM model was built with the Keras deep learning framework, and the accuracy of the model was further improved by selecting appropriate super-parameters through experiments. Secondly, the flue gas oxygen content, as the leading variable, was combined with the mechanism and boiler process primary auxiliary variables. Based on the actual production data collected from a coal-fired power plant in Yulin, China, the data sets were preprocessed. Moreover, a selection model of auxiliary variables based on grey relational analysis is proposed to construct a new data set and divide the training set and testing set. Finally, this model is compared with the traditional soft-sensing modelling methods (i.e. the methods based on support vector machine and BP neural network). The RMSE of LSTM model is 4.51% lower than that of GA-SVM model and 3.55% lower than that of PSO-BP model. The conclusion shows that the oxygen content model based on LSTM has better generalization and has certain industrial value.

scholarly journals HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

2020 ◽  
Vol 20 (4) ◽  
pp. 55-73
Author(s):  
K. Dinesh Kumar ◽  
E. Umamaheswari
Keyword(s):  
Short Term Memory ◽  
Hybrid Approach ◽  
Differential Evolution Algorithm ◽  
Memory Loss ◽  
Data Sets ◽  
Workload Prediction ◽  
Workload Management ◽  
Management Framework ◽  
Multilayer Perceptron Network ◽  
Lstm Network

AbstractFor cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach’s effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.

The Development of Building Materials Recommendation System Based on Collaborative Filtering

2013 ◽  
Vol 281 ◽  
pp. 597-602 ◽  
Author(s):  
Guo Fang Kuang ◽  
Chun Lin Kuang
Keyword(s):  
Collaborative Filtering ◽  
Building Materials ◽  
Recommendation System ◽  
High Strength ◽  
Data Sets ◽  
User Interest ◽  
Insulation Materials ◽  
Target User ◽  
Wide Range ◽  
Materials Used

The building materials used in building materials collectively referred to as building materials. New building materials, including a wide range of insulation materials, insulation materials, high strength materials, breathing material belong to the new material. Collaborative filtering process is based on known user evaluation to predict the target user interest in the target, and then recommended to the target user. This paper proposes the development of building materials recommendation system based on Collaborative filtering. Experimental data sets prove that the proposed algorithm is effective and reasonable.

scholarly journals Research on User Interest Expression and Recommendation Service based on Three-dimensional Relationship of Users and Items

2020 ◽  
Vol 8 (5) ◽  
pp. 01-15
Author(s):  
Dasong Sun ◽  
Shuqing Li ◽  
Wenjing Yan ◽  
Fusen Jiao ◽  
Junpeng Chen
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Memory Function ◽  
Three Dimensional ◽  
Matrix Decomposition ◽  
Data Sets ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Trust Network ◽  
Experimental Conditions

The existing recommendation algorithms often rely heavily on the original score information in the user rating matrix. However, the user's rating of items does not fully reflect the user's real interest. Therefore, the key to improve the existing recommendation system algorithm effectively is to eliminate the influence of these unfavorable factors and the accuracy of the recommendation algorithm can be improved by correcting the original user rating information reasonably. This paper makes a comprehensive theoretical analysis and method design from three aspects: the quality of the item, the memory function of the user and the influence of the social friends trusted by the user on the user's rating. Based on these methods, this paper finally proposes a collaborative filtering recommendation algorithm (FixCF) based on user rating modification. Using data sets such as Movielens, Epinions and Flixster, the data sets are divided into five representative subsets, and the experimental demonstration is carried out. FixCF and classical collaborative filtering algorithms, existing matrix decomposition-based algorithms and trust network-based inference are compared. The experimental results show that the accuracy and coverage of FixCF have been improved under many experimental conditions.

Topic recommendation system using personalized fuzzy logic interest set

2020 ◽  
pp. 1-11
Author(s):  
Wenqiang Zhu
Keyword(s):  
Fuzzy Logic ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
User Behavior ◽  
Experimental Tests ◽  
User Interest ◽  
Fuzzy Logic Systems ◽  
Fine Grained ◽  
User Interests ◽  
Collaborative Recommendation

First, the recommendation system and its advantages are introduced in detail, and based on the characteristics of the intelligent topic logical interest set resource and user behavior in the existing intelligent topic logical interest set resource platform, a personalized fuzzy logic model of the intelligent topic logical interest set resource is established and adapted to it. The personalized fuzzy logic user personalized fuzzy logic interest model of personalized fuzzy logic is designed, and the user personalized fuzzy logic interest transfer method is designed to simulate the user learning process. Secondly, on the basis of the established model, according to the idea of collaborative filtering, the personalized fuzzy logic user’s personalized fuzzy logic interest value and the user’s rating of resources are respectively predicted, and the two prediction results are combined to recommend resources to the user. Finally, the ontology is applied to user interest description, and a method based on personalized fuzzy logic user rough interest vector and nearest neighbor concept aggregation is proposed to find fine-grained user interest and recommend interest resources. Experimental tests show that this method can better describe the composition and development of user interests, making the recommendation effect of interest resources for specific users more accurate and reliable. The problem of collaborative recommendation in personalized fuzzy logic systems is further studied, the basic principles and typical technologies of collaborative recommendation are analyzed, and the collaborative recommendation method based on users with similar interests and the collaborative recommendation method based on weighted association rules are proposed.

scholarly journals The Development of E-Commerce Recommendation System Based on Collaborative Filtering

2012 ◽  
Vol 6-7 ◽  
pp. 636-640 ◽  
Author(s):  
Guo Fang Kuang
Keyword(s):  
Experimental Data ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Product Information ◽  
Data Sets ◽  
User Evaluation ◽  
User Interest ◽  
Target User ◽  
Purchase Process ◽  
Sales Staff

The recommendation system in the e-commerce is to provide customers with product information and recommendations to help customers decide what to buy goods and analog sales staff to recommend merchandise to complete the purchase process. Collaborative filtering process is based on known user evaluation to predict the target user interest in the target, and then recommended to the target user. This paper proposes the development of E-commerce recommendation system based on Collaborative filtering. Experimental data sets prove that the proposed algorithm is effective and reasonable.

scholarly journals Research on recommendation algorithm of Graph attention Network based on Knowledge graph

2021 ◽  
Vol 2113 (1) ◽  
pp. 012085
Author(s):  
Yiping Zeng ◽  
Shumin Liu
Keyword(s):  
Recommendation System ◽  
Auxiliary Information ◽  
Experimental Comparison ◽  
Knowledge Graph ◽  
Data Sets ◽  
User Interest ◽  
Attention Network ◽  
Model Combining ◽  
Public Data ◽  
New Research

Abstract The introduction of knowledge graph as the auxiliary information of recommendation system provides a new research idea for personalized intelligent recommendation. However, most of the existing knowledge graph recommendation algorithms fail to effectively solve the problem of unrelated entities, leading to inaccurate prediction of potential preferences of users. To solve this problem, this paper proposes a KG-IGAT model combining knowledge graph and graph attention network, and adds an interest evolution module to graph attention network to capture user interest changes and generate top-N recommendations. Finally, experimental comparison between the proposed model and other algorithms using public data sets shows that KG-IGAT has better recommendation performance.

Fast imaging of time-domain airborne EM data using deep learning technology

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. E163-E170
Author(s):  
Jinfeng Li ◽  
Yunhe Liu ◽  
Changchun Yin ◽  
Xiuyan Ren ◽  
Yang Su
Keyword(s):  
Time Domain ◽  
High Speed ◽  
Short Term Memory ◽  
Back Propagation ◽  
Imaging Method ◽  
Data Sets ◽  
Learning Technology ◽  
Error Back Propagation ◽  
Imaging Result ◽  
Airborne Electromagnetic

Due to the huge amount of data generated by time-domain airborne electromagnetic (AEM) systems, conductivity depth imaging methods are widely used to help in the interpretation of these data because they can be generated quickly and easily. We have introduced a new imaging method generated using a deep neural network. The network structure combines four convolutional neural networks with a long short-term memory technique and adopts an error back-propagation scheme to update the parameters. A deep hierarchical structure is used to extract and store the complex nonlinear relationship between the model and electromagnetic (EM) responses, creating results close to 1D inversions. To check the effectiveness, we have examined our algorithm on synthetic and survey AEM data. The imaging result shows that this method, when using reasonable network parameters, can not only image well at high speed, but the method also is not very sensitive to noise. Another advantage of this method is that once the training is completed on a well-distributed training set, the network can be used without any changes to process other data sets from an identically configured AEM system.

scholarly journals Sarcasm Analysis Using Conversation Context

2018 ◽  
Vol 44 (4) ◽  
pp. 755-792 ◽  
Author(s):  
Debanjan Ghosh ◽  
Alexander R. Fabbri ◽  
Smaranda Muresan
Keyword(s):  
Social Media ◽  
Computational Models ◽  
Human Performance ◽  
Short Term Memory ◽  
Data Sets ◽  
Discussion Forums ◽  
Sentence Level ◽  
Social Media Platforms ◽  
Gold Label ◽  
Lstm Network

Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, the speaker’s sarcastic intent is not always apparent without additional context. Focusing on social media discussions, we investigate three issues: (1) does modeling conversation context help in sarcasm detection? (2) can we identify what part of conversation context triggered the sarcastic reply? and (3) given a sarcastic post that contains multiple sentences, can we identify the specific sentence that is sarcastic? To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn. We show that LSTM networks with sentence-level attention on context and current turn, as well as the conditional LSTM network, outperform the LSTM model that reads only the current turn. As conversation context, we consider the prior turn, the succeeding turn, or both. Our computational models are tested on two types of social media platforms: Twitter and discussion forums. We discuss several differences between these data sets, ranging from their size to the nature of the gold-label annotations. To address the latter two issues, we present a qualitative analysis of the attention weights produced by the LSTM models (with attention) and discuss the results compared with human performance on the two tasks.

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