A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Research on BP Neural Network Recommendation Model Fusing User Reviews and Ratings
2021
IEEE Access
Preserved Fulltext
This model improves the structure of BP neural network by specially designing the structure of network layers. ...
User reviews and ratings can be processed in two separate sub-networks respectively and further fused in the fusion layer. ...
The BP neural network can be divided into three layers, that is, input layer, hidden layer and output layer. A deep BP neural network usually has multiple hidden layers. ...
doi:10.1109/access.2021.3080079
fatcat:s3vu3bhkxbfmhfhzj7bp5euzzm
Applying Internet information technology combined with deep learning to tourism collaborative recommendation system
2020
PLoS ONE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The model accuracy is the highest with 40 hidden factors, 100 convolutions, and a 100+50 combination hidden layer. ...
The Convolutional Neural Network (CNN) is used to process review information of users and tourism service items. ...
Fig 5 shows that the model is divided into three parts: review information network of tourists; review information network of tourism service items; and other information networks. ...
doi:10.1371/journal.pone.0240656
pmid:33271589
pmcid:PMC7714558
fatcat:v64dhfak7bgyrauvupdratsv4a
DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation
2020
Complexity
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this framework, we utilize multiple types of deep neural networks that are best suited for each type of heterogeneous inputs and introduce an extra layer to obtain the joint representations for users ...
In this paper, we propose a novel deep neural architecture named DeepFusion to jointly learn user and item representations from numerical ratings, textual reviews, and item metadata. ...
A popular research line is the joint modeling of numerical ratings and textual reviews for recommendation. Textual reviews are able to express user opinions towards various item features. ...
doi:10.1155/2020/4780191
fatcat:r5pixfcteze3hi46bdouxjnlp4
Fusing User Reviews Into Heterogeneous Information Network Recommendation Model
2022
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
However, most of the current recommendation models fail to make the most of the ample resources hidden behind auxiliary data and user reviews. For this reason, we put forward the FHRec model. ...
As an effective method to deal with this dilemma of information overload, recommendation system has become a popular area of research for the past few years. ...
Specifically, through a two-layer neural network learning attention scores for the features learned from different meta-paths and the features extracted from reviews. ...
doi:10.1109/access.2022.3176727
fatcat:n5s435ezezfgffjkcinc4oug5u
Customer Reviews Analysis with Deep Neural Networks for E-Commerce Recommender Systems
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Then, we employed a deep neural network to extract deep features from the reviews-characteristics matrix to deal with sparsity, ambiguity, and redundancy. ...
Written customer review is a rich source of information that can offer insights into the recommender system. ...
In order to transform this architecture into an autoencoder using a deep neural network, we need to use more layers as hidden layers where the output of each layer is the input for the next layer. ...
doi:10.1109/access.2019.2937518
fatcat:kmqunio5jrahnkgpupin5lfbta
Joint Deep Model with Multi-Level Attention and Hybrid-Prediction for Recommendation
2019
Entropy
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Based on these two designs, we build a Multi-level Attentional and Hybrid-prediction-based Recommender (MAHR) model for recommendation. ...
However, it is still a challenge for RS to extract the most informative feature from a tremendous amount of reviews. ...
In the future, we plan to combine the transfer learning and the latent factor model to build a more robust prediction layer for recommender system. ...
doi:10.3390/e21020143
pmid:33266859
fatcat:xtj7vkbk6fgebaqytfgj5j2xvm
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. ...
A shared layer is introduced on the top to couple these two networks together. ...
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research. ...
arXiv:1701.04783v1
fatcat:jfdnrashgzeq7pa4dtmbvtmyhy
Joint Deep Network With Auxiliary Semantic Learning for Popular Recommendation
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a new joint deep network model with auxiliary semantic learning for the popular recommendation algorithm (DMPRA). ...
First, we define the items with a large quantity of review data and high ratings as the popular recommended items. Second, we introduce text analysis into the popular recommendation algorithm. ...
ACKNOWLEDGMENT The authors would like to thank the editor and the anonymous reviewers for constructive suggestions. ...
doi:10.1109/access.2020.2976498
fatcat:7y5kidjvzfdzzhm5qfv35h7hjm
A Parallel Deep Neural Network Using Reviews and Item Metadata for Cross-domain Recommendation
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
CD-DNN builds a single mapping for user features in the latent space, so that the network for user is optimized together with item features from other domains. ...
In this paper, we propose Crossdomain Deep Neural Network (CD-DNN) for the cross-domain recommendation. ...
In the next three subsections, we only focus on introducing the process for the network Net u because the networks of Net u and Net i are almost identical; they differ only in input.
1) EMBEDDING LAYER ...
doi:10.1109/access.2020.2977123
fatcat:4lfqtfpt4jdplf2j66do2p325y
Recommendation algorithm combining ratings and comments
2021
Alexandria Engineering Journal
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
feature vector; finally, it is fed into a factorization machine and a fully connected network for scoring prediction. ...
The rating data of recommendation systems are too sparse, and many existing studies introduce review information to alleviate this phenomenon, but the connection between reviews and the importance of each ...
learning approach for modeling reviews, using two parallel convolutional neural networks for feature extraction of user-item reviews separately, and then feeding the obtained features into a factorization ...
doi:10.1016/j.aej.2021.04.056
fatcat:3cnjrvyjrbauxoykoir5gxpuq4
Review-Based Cross-Domain Collaborative Filtering: A Neural Framework
2019
ACM Conference on Recommender Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Most current crossdomain recommenders focus on modeling user ratings but pay limited attention to user reviews. ...
CCS CONCEPTS • Information systems → Recommender systems; • Computing methodologies → Neural networks. ...
hybrid model unifies both ratings and reviews under a neural framework. • Cross-domain neural network (CDN) [3] : This model utilizes neural networks for cross domain recommendation system. ...
dblp:conf/recsys/DoanS19
fatcat:u4cj7f2f55d57ojmstaw6jsufq
Deep Personalized Medical Recommendations Based on the Integration of Rating Features and Review Sentiment Analysis
2021
Wireless Communications and Mobile Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a personalized medical recommendation method based on a convolutional neural network that integrates revised ratings and review text, called revised rating and review based on ...
a convolutional neural network (RR&R-CNN). ...
In the input layer of the convolutional neural network model, a user's review text of doctors and the revised rating are combined as input features. ...
doi:10.1155/2021/5551318
fatcat:ultkhl7mlrdzbgzo63at7l6fr4
A Heterogeneous Graph Neural Model for Cold-start Recommendation
2020
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
A Heterogeneous Graph Neural Model for Cold-start Recommendation . In ...
predicted from the social network and textual reviews. ...
For a fair comparison, we set the number of neural network layers of the models including NeuMF, NGCF and HGNR to 3. ...
doi:10.1145/3397271.3401252
dblp:conf/sigir/LiuOMM20
fatcat:pcogjke2ivgg5bfbxrbirkunuy
Hierarchical User and Item Representation with Three-Tier Attention for Recommendation
2019
Proceedings of the 2019 Conference of the North
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In addition, we incorporate a three-tier attention network in our model to select important words, sentences and reviews. ...
In this paper, we propose a hierarchical user and item representation model with threetier attention to learn user and item representations from reviews for recommendation. ...
Conclusion In this paper, we propose a hierarchical user and item representation model with three-tier attention to learn user and item representations from reviews for recommendation. ...
doi:10.18653/v1/n19-1180
dblp:conf/naacl/WuWLH19
fatcat:rjccuuj6s5hu7kw4dagjlxbovq
Sentiment Neural Network(SNN)-for Knowledge based Recommender System
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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. ...
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. ...
The predictive model design for the SNN excellence creates planned usage of Review related data in text form. The SNN input and output layers functionality and forward neural network is the similar. ...
doi:10.35940/ijitee.a4196.129219
fatcat:zkjq7rsbsbgpfcs2d67bercxhi
« Previous
Showing results 1 — 15 out of 220,903 results