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).

scholarly journals Web crawling based context aware recommender system using optimized deep recurrent neural network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Venugopal Boppana ◽  
P. Sandhya
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recommender System ◽  
Recommendation System ◽  
Contextual Information ◽  
Recommendation Systems ◽  
User Preferences ◽  
Bald Eagle ◽  
Context Aware ◽  
Deep Recurrent Neural Network

AbstractRecommendation systems are obtaining more attention in various application fields especially e-commerce, social networks and tourism etc. The top items are recommended based on the ability of recommender system which predict the future preference out of the available items. Because of the internet, the people in the current society has too many options that’s why the recommendation system is very essential. The recommendation is achieved by the particular users who predict the ratings for numerous items and recommend those items to other users. Majorly, content and collaborative filtering techniques are employed in typical recommendation systems to find user preferences and provide final recommendations. But, these systems commonly lacks to take growing user preferences in various contextual factors. Context aware recommendation systems consider various contextual parameters into account and attempt to catch user preferences appropriately. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. Therefore, in this paper an effective deep learning based context aware recommendation model is proposed which can be act as an efficient recommender system by showing minimum error during recommendation. Initially, the dataset is pre-processed using Natural Language Tool Kit (NLTK) in Python platform. After pre-processing, the TF–IDF and word embedding model is used for every pre-processed reviews to extract the features and contextual information. The extracted feature is considered as an input of density based clustering to group the negative, neutral and positive sentiments of user reviews. Finally, deep recurrent neural Network (DRNN) is employed to get the most preferable user from every cluster. The recurrent neural network model parameter values are initialized through the fitness computation of Bald Eagle Search (BES) algorithm. The proposed model is implemented using NYC Restaurant Rich Dataset using Python programming platform and performance is evaluated based on the metrics of accuracy, precision, recall and compared with existing models. The proposed recommendation model achieves 99.6% accuracy which is comparatively higher than other machine learning models.

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.

A New Contextual Influencer User Measure to Improve the Accuracy of Recommender System

2018 ◽  
Vol 9 (4) ◽  
pp. 38-51
Author(s):  
Maryam Jallouli ◽  
Sonia Lajmi ◽  
Ikram Amous
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Recommendation System ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
User Model ◽  
Context Aware ◽  
Additional Information ◽  
Context Element ◽  
Cold Start Problem

In the last decade, social-based recommender systems have become the best way to resolve a user's cold start problem. In fact, it enriches the user's model by adding additional information provided from his social network. Most of those approaches are based on a collaborative filtering and compute similarities between the users. The authors' preliminary objective in this work is to propose an innovative context aware metric between users (called contextual influencer user). These new similarities are called C-COS, C-PCC and C-MSD, where C refers to the category. The contextual influencer user model is integrated into a social based recommendation system. The category of the items is considered as the most pertinent context element. The authors' proposal is implemented and tested within the food dataset. The experimentation proved that the contextual influencer user measure achieves 0.873, 0.874, and 0.882 in terms of Mean Absolute Error (MAE) corresponding to C-cos, C-pcc and C-msd, respectively. The experimental results showed that their model outperforms several existing methods.

scholarly journals Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application

2017 ◽  
Author(s):  
Sara Saeedi ◽  
Xueyang Zou ◽  
Mariel Gonzales ◽  
Steve Liang
Keyword(s):  
Web Application ◽  
Management Practices ◽  
Recommendation System ◽  
Contextual Information ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Context Information ◽  
Context Aware ◽  
Context Reasoning ◽  
Past Behavior

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

scholarly journals EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Pengcheng Ma ◽  
Qian Gao
Keyword(s):  
Recommendation System ◽  
Rapid Development ◽  
User Preferences ◽  
Feature Interaction ◽  
Eeg Signal ◽  
Machine Model ◽  
Data Set ◽  
Eeg Data ◽  
Factorization Machine

In recent years, with the development of brain science and biomedical engineering, as well as the rapid development of electroencephalogram (EEG) signal analysis methods, using EEG signals to monitor human health has become a very popular research field. The innovation of this paper is to analyze the EEG signal for the first time by building a depth factorization machine model, so that on the basis of analyzing the characteristics of user interaction, we can use EEG data to predict the binomial state of eyes (open eyes and closed eyes). The significance of the research is that we can diagnose the fatigue and the health of the human body by detecting the state of eyes for a long time. On the basis of this inference, the proposed method can make a further useful auxiliary support for improving the accuracy of the recommendation system recommendation results. In this paper, we first extract the features of EEG data by wavelet transform technology and then build a depth factorization machine model (FM+LSTM) which combines factorization machine (FM) and Long Short-Term Memory (LSTM) in parallel. Through the test of real data set, the proposed model gets more efficient prediction results than other classifier models. In addition, the model proposed in this paper is suitable not only for the determination of eye features but also for the acquisition of interactive features (user fatigue) in the recommendation system. The conclusion obtained in this paper will be an important factor in the determination of user preferences in the recommendation system, which will be used in the analysis of interactive features by the graph neural network in the future work.

scholarly journals An English Teaching Resource Recommendation System Based on Network Behavior Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jing Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recommendation System ◽  
User Behavior ◽  
Teaching Resources ◽  
English Teaching ◽  
Collaborative Recommendation ◽  
Proposed Model ◽  
Deep Learning Model ◽  
Local User

The sharing of English teaching resources has always been a concern. In order to further improve the value of different English teaching resources, this paper proposes a resource management system based on an improved collaborative recommendation algorithm. The proposed model can predict user behavior based on deep learning models of graph neural network (GNN) and recurrent neural network (RNN). The graph neural network can capture the hidden state of local user behavior and be used as a preprocessing step. Recurrent neural networks can capture time series information. Therefore, the model is constructed by combining GNN and RNN to obtain the advantages of both. In order to prove the effectiveness of the model, we used CNGrid’s real user behavior dataset in the experiment and finally compared the results with other methods. The different deep learning-based models achieved a precision of up to 88% and outperformed other traditional models. The experimental results show that this new deep learning model has good sharing value.

scholarly journals Sentiment Neural Network(SNN)-for Knowledge based Recommender System

2019 ◽  
Vol 9 (2) ◽  
pp. 4297-4304
Keyword(s):  
Neural Network ◽  
Recommender System ◽  
Recommendation System ◽  
Weighting Factor ◽  
Vital Role ◽  
User Preferences ◽  
Machine Learning Techniques ◽  
Second Phase ◽  
Training Time ◽  
Discrete Values

Recommendation systems play a crucial role in e-commerce by recommending products that suit for the consumers also providing exact information to users. For the past few decades, researchers used many machine learning techniques on recommender system. In recommender system ML based algorithms providing a better detection of user preferences, item features and users-items. In this way deep learning play an increasingly vital role in review recommender systems, since they used a bunch of discrete values for review. However, a problem arises regarding that feedbacks. Discrete values are those which are hard to describe user’s interests. This problem makes it impossible to specifically model user’s choice for recommendation. Purpose of this paper is to introduce a new novel SNN (Sentiment Neural Network) framework for effective recommendation system. SNN frameworks consider two phases. In first phase of SNN framework, this work introduces NLP techniques for converting unstructured data into structured data. This technique includes pre-processing data, feature extraction, word scoring, polarity classification and sentiment analysis. The second phase of SNN framework used for verifying the polarity classes with the real world examples. SNN structures are not only among the best models with reference to prediction accuracy, they also consider the weighting factor on classifier to reduce the training time. A novel Sentiment Neural Network along with knowledge recommender system is suggested for review features extraction, text classification and analyzing review features in the various domains.

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