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Learning to Generate Product Reviews from Attributes

Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, Ke Xu
2017 Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers  
This paper presents an attention-enhanced attribute-to-sequence model to generate product reviews for given attribute information, such as user, product, and rating.  ...  The attribute encoder learns to represent input attributes as vectors. Then, the sequence decoder generates reviews by conditioning its output on these vectors.  ...  The model learns to compute the likelihood of generated reviews given input attributes.  ... 
doi:10.18653/v1/e17-1059 dblp:conf/eacl/ZhouLWDHX17 fatcat:xdb5wkr5ijexfda6ilwpwvy4pa

Generalized Ranking for Product Aspects using Concept Hierarchies

2015 International Journal of Science and Research (IJSR)  
from the consumer reviews.  ...  In this paper, we propose a novel framework to determine the ranking of products based on aggregation we simply use concept hierarchy to determine the products in categorical attributes with ranking taken  ...  Even retailers also encourage to take reviews from the consumers to promote their products in all aspects.  ... 
doi:10.21275/v4i11.nov151397 fatcat:t6ychwx54beltf56b77fnqnr6e

Intelligent Recommender System for Big Data Applications Based on the Random Neural Network

Will Serrano
2019 Big Data and Cognitive Computing  
On average, IRS outperforms the Big Data recommender systems after learning iteratively from its customer.  ...  Web users cannot be guaranteed that the products provided by recommender systems within Big Data are either exhaustive or relevant to their needs.  ...  , Machine Learning repository, Centre for Machine Learning and Intelligent Systems and Amazon data set from Julian McAuley Computer Science Department at University of California, San Diego.  ... 
doi:10.3390/bdcc3010015 fatcat:2ravfpgskza7ljkos4fp4eq2jm

Meaningful Answer Generation of E-Commerce Question-Answering [article]

Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan
2020 arXiv   pre-print
In this paper, we focus on the task of product-aware answer generation, which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.  ...  product reviews, product attributes, and a prototype answer into consideration.  ...  ACKNOWLEDGMENTS We would like to thank the anonymous reviewers for their constructive comments. We would also like to thank Anna  ... 
arXiv:2011.07307v1 fatcat:vweubhp7wbg4rjclgyat7atsey

Probing Product Description Generation via Posterior Distillation [article]

Haolan Zhan, Hainan Zhang, Hongshen Chen, Lei Shen, Zhuoye Ding, Yongjun Bao, Weipeng Yan, Yanyan Lan
2021 arXiv   pre-print
Existing works tend to generate the product description solely based on item information, i.e., product attributes or title words, which leads to tedious contents and cannot attract customers effectively  ...  To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews.  ...  Acknowledgements The authors would like to thank Hengyi Cai from Institute of Computing Technology, Chinese Academy of Sciences, and the anonymous reviewers for their constructive comments and suggestions  ... 
arXiv:2103.01594v1 fatcat:3x73nvv2fbho3pweeoru3zk54q

Product-Aware Answer Generation in E-Commerce Question-Answering [article]

Shen Gao, Zhaochun Ren, Yihong Eric Zhao, Dongyan Zhao, Dawei Yin, Rui Yan
2019 arXiv   pre-print
In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.  ...  Unlike existing question-answering problems, answer generation in e-commerce confronts three main challenges: (1) Reviews are informal and noisy; (2) joint modeling of reviews and key-value product attributes  ...  ACKNOWLEDGMENTS We would like to thank the anonymous reviewers for their constructive comments.  ... 
arXiv:1901.07696v2 fatcat:lskfjm4jtzdhzo6y2mggc7cify

Cyclegen: Cyclic consistency based product review generator from attributes

Vasu Sharma, Harsh Sharma, Ankita Bishnu, Labhesh Patel
2018 Proceedings of the 11th International Conference on Natural Language Generation  
The use of 'soft' generation and cyclic consistency allows us to train our model in an end to end fashion. We demonstrate the working of our model on product reviews from Amazon dataset.  ...  In this paper we present an automatic review generator system which can generate personalized reviews based on the user identity, product identity and designated rating the user wishes to allot to the  ...  Figure 1 : The model first learns attribute embeddings and then uses an LSTM network to generate the reviews one word at a time.  ... 
doi:10.18653/v1/w18-6552 dblp:conf/inlg/SharmaSBP18 fatcat:lzzsloil5nghje7zcsurweivuu

Learning to question

Mahashweta Das, Gianmarco De Francisci Morales, Aristides Gionis, Ingmar Weber
2013 Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13  
ShoppingAdvisor leverages both user preferences and technical product attributes in order to generate its suggestions.  ...  Second, we show how to employ a learning-to-rank approach in order to learn, for each node of the tree, a ranking of products appropriate to the users who reach that node.  ...  For each group of users, we also learn the product attribute weights and generate a ranking of top-k products in the training set, in order to identify the top products for recommendation.  ... 
doi:10.1145/2487575.2487653 dblp:conf/kdd/DasMGW13 fatcat:vt6yzmsrmrfxbn2hh6zhjlrrju

Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges [article]

Michael Saidani
2021 arXiv   pre-print
This contribution discusses the opportunities to reach and the challenges to address for building a machine learning pipeline, in order to get insights from product reviews to design more sustainable products  ...  insights from online product reviews automatically.  ...  How much can we learn from customers' product reviews to design more sustainable products? What design learnings could be elicited from online product review?  ... 
arXiv:2112.09391v1 fatcat:hrijcu6zirfytazsfwcjwai22i

Advantage of integration in big data: Feature generation in multi-relational databases for imbalanced learning

Farrukh Ahmed, Michele Samorani, Colin Bellinger, Osmar R. Zaiane
2016 2016 IEEE International Conference on Big Data (Big Data)  
Experiments are performed on a transactional dataset from a U.S. consumer electronics retailer to predict product returns and identify reasons behind those returns.  ...  In addition, we augmented the retail dataset with Supplier information and Reviews to show the value of data integration.  ...  We generated reviews to simulate a realistic situation where good products usually have good ratings and returned products are more likely to get bad reviews and some products might not have ratings at  ... 
doi:10.1109/bigdata.2016.7840644 dblp:conf/bigdataconf/AhmedSBZ16 fatcat:yxwoeguvg5bz7o4muol2ctsa2q

We know what you want to buy

Xin Wayne Zhao, Yanwei Guo, Yulan He, Han Jiang, Yuexin Wu, Xiaoming Li
2014 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14  
Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for  ...  makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews.  ...  Demographics Extraction from Online Product Reviews The first resource we consider is online product reviews.  ... 
doi:10.1145/2623330.2623351 dblp:conf/kdd/ZhaoGHJWL14 fatcat:5b3rlswl3vbrxa3zzsj7e2ucci

Collectively Embedding Multi-Relational Data for Predicting User Preferences [article]

Nitish Gupta, Sameer Singh
2015 arXiv   pre-print
For example, additional information about businesses from reviews, categories, and attributes should be leveraged for predicting user preferences, even though this information is often inaccurate and partially-observed  ...  By learning a set of embeddings that are shared across all the relations, the model is able to incorporate observed information from all the relations, while also predicting all the relations of interest  ...  These extensions will enable us to support a wider variety of relations and databases; we will, for example, be able to model the complete Yelp schema, including attributes such as tips, locations, temporal  ... 
arXiv:1504.06165v1 fatcat:xzritz4r5fcmtbwvw4mi4aj2ue

Feature-Based Sentimental Analysis On Product Review System Using CUDA-BB Algorithm

2020 International Journal of Emerging Trends in Engineering Research  
Datasets are obtained from:/www.kaggle.com.The data set contains the following information or attributes from the 1. Product Title 2.Brand Name 3.Price 4.Rating 5. Text review 6. Review Votes.  ...  In general several methods of machine learning which classify emotions or reviews given by several customers. The data set includes the opinions of a product provided by various consumers.  ... 
doi:10.30534/ijeter/2020/237892020 fatcat:txev4pk5bfcnxmpfofekwsekmq

Sentiment Level Evaluation of 3D Handicraft Products Application for Smartphones Usage

Natinai Jinsakul, Cheng-Fa Tsai, Paohsi Wang
2021 Electronics  
product application usage; and (4) investigate attracting users' attention to handicraft products after using the proposed 3D handicraft product application.  ...  by participants to the handicraft products revealed that positive and strongly positive sentiment was described by 79.61% of participants.  ...  Acknowledgments: The authors would like to express their sincere gratitude to the anonymous reviewers for their useful comments and suggestions for improving the quality of this paper, as well as the Department  ... 
doi:10.3390/electronics10020199 fatcat:l3rtihcunfexrp4p5lvggckf24

Try This Instead: Personalized and Interpretable Substitute Recommendation [article]

Tong Chen, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang, Meng Wang
2020 arXiv   pre-print
Instead of directly modelling user-item interactions, we extract explicit and polarized item attributes from user reviews with sentiment analysis, whereafter the representations of attributes, users, and  ...  When a user is browsing a specific type of product (e.g., a laptop) to buy, the accurate recommendation of substitutes (e.g., better equipped laptops) can offer the user more suitable options to choose  ...  More recently, the trend of utilizing reviews has carried over to neural network approaches, such as RRN [49] that learns product embeddings from both the textual reviews and manually crafted features  ... 
arXiv:2005.09344v1 fatcat:uyr7sipndbambe7yb2anuye6fq
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