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Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)

Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J. Smola, Jing Jiang, Chong Wang
2014 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14  
This suggests that uncovering aspects and sentiments will allow us to gain a better understanding of users, movies, and the process involved in generating ratings.  ...  For instance, on IMDb users leave reviews, commenting on different aspects of a movie (e.g. actors, plot, visual effects), and expressing their sentiments (positive or negative) on these aspects in their  ...  By comparing with HFT, we examine which of them provides a better modeling of movie reviews. JMARS Jointly modeling aspects, ratings and sentiments. This is the full model discussed in Section 3.  ... 
doi:10.1145/2623330.2623758 dblp:conf/kdd/DiaoQWSJW14 fatcat:hc4kllwa75dptpp77boqo4glbm

Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering [article]

Chao-Yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola
2015 arXiv   pre-print
With this novel technique we propose a new Bayesian model for joint collaborative filtering of ratings and text reviews through a sum of simple co-clusterings.  ...  Quite often this can be accomplished by perusing both ratings and review texts, since it is the latter where the reasoning for specific preferences is explicitly expressed.  ...  JMARS [7] jointly models aspects, sentiments, items, reviews, and ratings based on insights in review structure. A related line of work models multi-aspect ratings [18, 27] .  ... 
arXiv:1512.01845v1 fatcat:3xixuzue3zbwlfccmvijpgi5fu

Herding Effect based Attention for Personalized Time-Sync Video Recommendation [article]

Wenmian Yang, Wenyuan Gao, Xiaojie Zhou, Weijia Jia, Shaohua Zhang, Yutao Luo
2019 arXiv   pre-print
Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for  ...  video recommendations.  ...  Smola, Jing Jiang, and Chong Wang, “Jointly modeling 2015CB352401; Chinese National Research Fund (NSFC) Key aspects, ratings and sentiments for movie recommendation Project No. 61532013  ... 
arXiv:1905.00579v1 fatcat:xfihjufdizfyfhqjof2rtzasuu

Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness

Kaisong Song, Wei Gao, Shi Feng, Daling Wang, Kam-Fai Wong, Chengqi Zhang
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA).  ...  Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews.  ...  Acknowledgments This work is supported by the National Natural Science Foundation of China (Grant No. 61370074, 61402091) and partially supported by the UGC, HK, under the GRF initiative (#14232816) and  ... 
doi:10.24963/ijcai.2017/382 dblp:conf/ijcai/SongGFWWZ17 fatcat:eqvxhx7iorfspi752n7dtxzxie

Consistent MetaReg: Alleviating Intra-task Discrepancy for Better Meta-knowledge

Pinzhuo Tian, Lei Qi, Shaokang Dong, Yinghuan Shi, Yang Gao
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
In the few-shot learning scenario, the data-distribution discrepancy between training data and test data in a task usually exists due to the limited data.  ...  Moreover, the proposed meta-regularization method could be readily inserted into existing optimization-based meta-learning models to learn better meta-knowledge.  ...  JMARS [Diao et al., 2014] generalize probabilistic matrix factorization by incorporating user-aspect and movie-aspect priors, enhancing recommendation quality by jointly modeling aspects, ratings and  ... 
doi:10.24963/ijcai.2020/373 dblp:conf/ijcai/PanLLZ20 fatcat:b6umsqkeqjhfddaaiw5egaie3u

TransRev: Modeling Reviews as Translations from Users to Items [chapter]

Alberto García-Durán, Roberto González, Daniel Oñoro-Rubio, Mathias Niepert, Hui Li
2020 Lecture Notes in Computer Science  
The embedding of a review is learned such that (a) it performs well as input feature of a regression model for sentiment prediction; and (b) it always translates the reviewer embedding to the embedding  ...  We propose TransRev, an approach to the product recommendation problem that integrates ideas from recommender systems, sentiment analysis, and multi-relational learning into a joint learning objective.  ...  This publication reflects only the author's views and the European Community is not liable for any use that may be made of the information contained herein.  ... 
doi:10.1007/978-3-030-45439-5_16 fatcat:qfa2rltopnexfdi3hoxe33l4fa

TransRev: Modeling Reviews as Translations from Users to Items [article]

Alberto Garcia-Duran, Roberto Gonzalez, Daniel Onoro-Rubio, Mathias Niepert, Hui Li
2018 arXiv   pre-print
The embedding of a review is learned such that (a) it performs well as input feature of a regression model for sentiment prediction; and (b) it always translates the reviewer embedding to the embedding  ...  We propose TransRev, an approach to the product recommendation problem that integrates ideas from recommender systems, sentiment analysis, and multi-relational learning into a joint learning objective.  ...  For example, JMARS [9] outperforms HFT on a movie recommendation data set but it is outperformed by HFT on data sets similar to those used in our work [31] . -Supervised approaches.  ... 
arXiv:1801.10095v2 fatcat:kqqiidp2jraq7lmn6jdilcyx2y

Coevolutionary Recommendation Model

Yichao Lu, Ruihai Dong, Barry Smyth
2018 Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18  
In this paper, we present a novel deep learning recommendation model, which co-learns user and item information from ratings and customer reviews, by optimizing matrix factorization and an attention-based  ...  However, the natural sparsity of user-item rating data can be problematic in many domains and settings, limiting the ability to generate accurate predictions and effective recommendations.  ...  (v) JMARS: Jointly Modeling Aspects, Ratings, and Sentiments (JMARS) [8] is another state-of-the-art probabilistic model that combines collaborative filtering and topic modeling.  ... 
doi:10.1145/3178876.3186158 dblp:conf/www/LuDS18 fatcat:bpixoia46vftvb2albidnhzjvy

Aspect-Aware Latent Factor Model

Zhiyong Cheng, Ying Ding, Lei Zhu, Mohan Kankanhalli
2018 Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18  
To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation.  ...  The aspect importance is then integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings.  ...  [12] propose an integrated graphical model called JMARS to jointly model aspects, ratings and sentiments for movie rating prediction.  ... 
doi:10.1145/3178876.3186145 dblp:conf/www/ChengDZK18 fatcat:lthtbtos6zhormbotzywellho4

MMALFM

Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose C. Kanjirathinkal, Mohan Kankanhalli
2019 ACM Transactions on Information Systems  
To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation.  ...  Then the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings.  ...  Research Foundation, Prime Minister's Office, Singapore under its International Research Centre in Singapore Funding Initiative; the National Natural Science Foundation of China (Grant No. 61603233); and  ... 
doi:10.1145/3291060 fatcat:4352lpe7ybawdc5bwlj5p2ss7m

Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation [article]

Zhichao Xu, Hansi Zeng, Qingyao Ai
2021 arXiv   pre-print
We adapt several SOTA review-based rating prediction models for top-N recommendation tasks and compare them to existing top-N recommendation models from both performance and efficiency.  ...  However, the optimal model structure to utilize textual reviews for E-commerce top-N recommendation is yet to be determined.  ...  Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS).  ... 
arXiv:2106.09665v2 fatcat:6jyktn5tmbchxmgezdhcsajqfm

Sense-Based Topic Word Embedding Model for Item Recommendation

Ya Xiao, Zhijie Fan, Chengxiang Tan, Qian Xu, Wenye Zhu, Fujia Cheng
2019 IEEE Access  
By combining topic distribution, social relationships, and users' interests and interactions, we propose a time-aware probabilistic model to profile a user's preference score on items.  ...  As a useful way to help users filter information and save time, item recommendation intends to recommend new items to users who tend to be interested.  ...  They divided the words into five groups: background, movie specific, aspect, aspect sentiment and general sentiment.  ... 
doi:10.1109/access.2019.2909578 fatcat:wg72fh7fl5bmre2n7akd24scem

Sentiment analysis using deep learning approaches: an overview

Olivier Habimana, Yuhua Li, Ruixuan Li, Xiwu Gu, Ge Yu
2019 Science China Information Sciences  
Recently, deep learning approaches have been proposed for different sentiment analysis tasks and have achieved state-of-the-art results.  ...  The traditional approaches for sentiment analysis are classified into two categories: lexicon-based and machine learning approaches [2, 4, 36] .  ...  The SSTb dataset consists of movie reviews collected 5) http://ai.stanford.edu/ ∼ amaas/data/sentiment/. 6) https://github.com/nihalb/JMARS/tree/master/data. 7) https://nlp.stanford.edu/sentiment/. from  ... 
doi:10.1007/s11432-018-9941-6 fatcat:nbevrfiyybhszirol2af26c6ve

Spider Taylor-ChOA: Optimized Deep Learning Based Sentiment Classification for Review Rating Prediction

Santosh Kumar Banbhrani, Bo Xu, Hongfei Lin, Dileep Kumar Sajnani
2022 Applied Sciences  
This paper devises a technique based on sentiment classification for predicting the review rating. Here, the review data are taken from the database.  ...  Concurrently, the features are considered input for the review rating prediction, which determines positive and negative reviews using the hierarchical attention network (HAN), and training is done using  ...  Acknowledgments: The authors appreciate and acknowledge anonymous reviewers for their reviews and guidance.  ... 
doi:10.3390/app12073211 fatcat:cn6bjoix6bbitkic7v2zvx26za