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Attentive Aspect Modeling for Review-Aware Recommendation

Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, Tat-Seng Chua
2019 ACM Transactions on Information Systems  
In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges.  ...  In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance.  ...  CONCLUSION AND FUTURE WORK In this paper, we presented a Attentive Aspect-based Recommendation Model (AARM), which carefully capture the interactions between aspects extracted from reviews for recommendation  ... 
doi:10.1145/3309546 fatcat:clljizpudnabvhireq5ba5hnza

Context-Aware Co-attention Neural Network for Service Recommendations

Lei Li, Ruihai Dong, Li Chen
2019 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)  
In this paper, we propose a novel model, named Context-aware Co-Attention Neural Network (CCANN), to dynamically infer relations between contexts and users/items, and subsequently to model the degree of  ...  matching between users' contextual preferences and items' context-aware aspects via coattention mechanism.  ...  As such, context-aware models can better characterize users' preferences over items' aspects for recommendation.  ... 
doi:10.1109/icdew.2019.00-11 dblp:conf/icde/LiDC19 fatcat:6iuq5zni7bc6pltewmcdp6aab4

A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction

Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, Mohan Kankanhalli
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
To tackle this problem, in this paper, we develop a novel aspect-aware recommender model named A$^3$NCF, which can capture the varying aspect attentions that a user pays to different items.  ...  Current recommender systems consider the various aspects of items for making accurate recommendations.  ...  . • We propose an aspect-aware rating prediction method based on a novel adaptive aspect attention modeling design.  ... 
doi:10.24963/ijcai.2018/521 dblp:conf/ijcai/ChengD0ZSK18 fatcat:ykagpwrxjvgtlmwovhmv5ehcs4

Improving Explainable Recommendations by Deep Review-Based Explanations

Sixun Ouyang, Aonghus Lawlor
2021 IEEE Access  
criteria of text-aware recommender systems.To make fair comparisons, we train review-aware recommender systems by human written reviews and attain advanced recommendations by feeding generated reviews  ...  For explanation evaluation, quantitative analyses reveal good understandable scores for our generated review-based explanations, and qualitative case studies substantiate we can capture critical aspects  ...  non-review aware recommender systems (RMSE) TABLE 4 : 4 RMSE Performance for review aware recommender systems.  ... 
doi:10.1109/access.2021.3076146 fatcat:6eedwpdhjbfsvp52alb6zlsdoy

A recommendations model with multiaspect awareness and hierarchical user-product attention mechanisms

Zhongqin Bi, Shuming Dou, Zhe Liu, Yongbin Li
2020 Computer Science and Information Systems  
Then, we design an attention network to reflect the adaptive change in the user preferences for each aspect of the targeted product in terms of the rating and review.  ...  To overcome this limitation, this paper proposes a novel model to capture the varying attention of a user for different products by using a multilayer attention framework.  ...  To sum up, the main contributions of this work are as follows. • We propose a multiaspect awareness model for recommendations.  ... 
doi:10.2298/csis190925024b fatcat:zr5dyd4iy5hwlgvve7ryxtv3yu

Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations

Jianmo Ni, Julian McAuley
2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
We incorporate aspect-level information via an aspect encoder that learns 'aspect-aware' user and item representations.  ...  Experimental results show that our model is capable of generating coherent and diverse reviews that expand the contents of input phrases.  ...  In this paper, we focus on designing a review generation model that is able to leverage both user and item information as well as auxiliary, textual input and aspect-aware knowledge.  ... 
doi:10.18653/v1/p18-2112 dblp:conf/acl/NiM18 fatcat:3qdol2kuprhs5auijaplrpe63e

Multilingual Review-aware Deep Recommender System via Aspect-based Sentiment Analysis

Peng Liu, Lemei Zhang, Jon Atle Gulla
2021 ACM Transactions on Information Systems (TOIS; Formerly: ACM Transactions on Office Information Systems)  
To this end, we propose a novel Multilingual Review-aware Deep Recommendation Model (MrRec) for rating prediction tasks.  ...  Recent studies that leverage deep-learning techniques for review-aware RSs have demonstrated their effectiveness in modelling fine-grained user-item interactions through the aspects of reviews.  ...  ACKNOWLEDGMENTS We sincerely thank the anonymous reviewers for their valuable comments and suggestions.  ... 
doi:10.1145/3432049 fatcat:xstqs34hujh5jhdxrbnxy55rqa

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [article]

Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
2020 arXiv   pre-print
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.  ...  This survey provides a systemic summary of three categories of trust-aware recommender systems: social-aware recommender systems that leverage users' social relationships; robust recommender systems that  ...  We outline three aspects of trust, i.e. social-awareness, robustness, and explainability. For each aspect, we present the literature review and summarize the related deep learning-based techniques.  ... 
arXiv:2004.03774v2 fatcat:q7mehir7hbbzpemw3q5fkby5ty

An End-to-End Review-Based Aspect-Level Neural Model for Sequential Recommendation

Yupeng Liu, Yanan Zhang, Xiaochen Zhang, Stefania Tomasiello
2021 Discrete Dynamics in Nature and Society  
This paper models users' long-term and short-term preferences through aspect-level reviews using a sequential neural recommendation model.  ...  Specifically, the model is devised to encode users and items with the aspect-aware representations extracted globally and locally from the user-related and item-related reviews.  ...  Recommendation models include review-based model [1] , attention-based model [2] [3] [4] , aspect-based model [5] , etc [6] [7] [8] .  ... 
doi:10.1155/2021/6693730 fatcat:7uysu37iwfdtnb7cggibmjjhhq

A Knowledge-Aware Attentional Reasoning Network for Recommendation

Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, Li Guo
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Knowledge-graph-aware recommendation systems have increasingly attracted attention in both industry and academic recently.  ...  In this paper, we propose a knowledge-aware attentional reasoning network KARN that incorporates the users' clicked history sequences and path connectivity between users and items for recommendation.  ...  Acknowledgments This work is supported by National Key R&D Program No.2017YFB0803003, and the National Natural Science Foundation of China (No.61202226), We thank all anonymous reviewers for their constructive  ... 
doi:10.1609/aaai.v34i04.6184 fatcat:4sp67naae5ezrhahfoknew6h54

Fusing Knowledge and Aspect Sentiment for Explainable Recommendation

Peng Bai, Yang Xia, Yongsheng Xia
2020 IEEE Access  
[30] proposed an attention-based LSTM for aspect sentiment classification, which mainly uses the attention mechanism to capture the importance of different context information for a given aspect.  ...  In DER, a time-aware GRU is used to model user dynamic preferences and CNN is used to analyze the information of item review. Except text, image is another display format of recommended reason.  ... 
doi:10.1109/access.2020.3012347 fatcat:ifnchobdj5h4xekxykmrrz2nly

Aspect-based Fashion Recommendation with Attention Mechanism

Weiqian Li, Bugao Xu
2020 IEEE Access  
In this paper, we proposed an aspect-based fashion recommendation model with attention mechanism (AFRAM) to predict customer ratings based on online reviews of fashion products.  ...  Online review is a powerful source for understanding users' shopping experiences, preferences and feedbacks on product/item performances, and thus is useful for enhancing personalized recommendations for  ...  of neural attention and co-attention by including an aspect-aware representation from a learning component and an estimator of aspect importance. 5) DAML [62] : The model utilizes local and mutual attentions  ... 
doi:10.1109/access.2020.3013639 fatcat:ncidmsiyyrb23c6vz54huxbcee

An Attentive Memory Network Integrated with Aspect Dependency for Document-Level Multi-Aspect Sentiment Classification

Qingxuan Zhang, Chongyang Shi
2019 Asian Conference on Machine Learning  
Unlike recent proposed models which average word embeddings of aspect keywords to represent aspect and utilize hierarchical architectures to encode review documents, we adopt attention-based memory networks  ...  In this paper, we propose an attentive memory network for document-level multi-aspect sentiment classification.  ...  The Attention Weights of Sentences We sample a review document from TripAdvisor and visualize the aspect attention for case study.  ... 
dblp:conf/acml/ZhangS19 fatcat:4lx5p7oxh5gb7ilguwzg5pdroy

Propagation-aware Social Recommendation by Transfer Learning [article]

Haodong Chang, Yabo Chu
2021 arXiv   pre-print
for recommendation by an attention mechanism.  ...  Social-aware recommendation approaches have been recognized as an effective way to solve the data sparsity issue of traditional recommender systems.  ...  SAMN [8] : A state-of-the-art deep learning method leverages attention mechanism to model both aspect-and friend-level differences for the social-aware recommendation.  ... 
arXiv:2107.04846v1 fatcat:v5dkffhuzfepfjh42glc7w7lfi

A Context-Aware User-Item Representation Learning for Item Recommendation [article]

Libing Wu, Cong Quan, Chenliang Li, Qian Wang, Bolong Zheng
2017 arXiv   pre-print
In this paper, we propose a novel context-aware user-item representation learning model for rating prediction, named CARL.  ...  In review-based learning component, with convolution operations and attention mechanism, the relevant features for a user-item pair are extracted by jointly considering their corresponding reviews.  ...  THE PROPOSED MODEL In this section, we present CARL, a context-aware user-item representation learning model for item recommendation.  ... 
arXiv:1712.02342v5 fatcat:o2nj3msqxbfsde4vtme3zbmium
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