Review Sentiment-Guided Scalable Deep Recommender System

Hyun, Dongmin; Park, Chanyoung; Yang, Min-Chul; Song, Ilhyeon; Lee, Jung-Tae; Yu, Hwanjo

doi:10.1145/3209978.3210111

Cited by 32 publications

(27 citation statements)

References 12 publications

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“…This paper uses only U c to construct the dataset. The Amazon Dataset is collected by Julian McAuley [25], [19], which contains 142.8 million reviews and overall rating spanning 1996.05∼2014.07. Each piece of data contains uid, iid, rating, and review, where uid is the user ID, iid is the product ID, rating is the overall rating, and review is the text that the user wrote for the product.…”

Section: Methodsmentioning

confidence: 99%

“…However, it's a single domain recommendation algorithm, which is not suitable for the cold-start user. Sen-tiRec [19] incorporates the sentiment information of review text when modeling the user preference and product feature, which is more scalable. MV-DNN [21] model is proposed to map users and items to a latent space where the similarity between users and their preferred items is maximized.…”

Section: Related Work a Deep Learning Recommender Systemmentioning

confidence: 99%

“…The emotion of review text with a rating greater or equal to 3 is regarded as positive. Inspired by SentiRec [19], we set up a CNN to incorporate such sentiment information when encoding each review text into vector form. The architecture is shown in Fig.2.…”

Section: A Review Encodermentioning

confidence: 99%

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RACRec: Review Aware Cross-Domain Recommendation for Fully-Cold-Start User

et al. 2020

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Traditional recommendation algorithms such as matrix factorization, collaborative filtering perform poorly when lack of interactive information of user and product, known as the user cold-start problem, which may cut down the revenue of E-Commerce platform. Moreover, it is more challenging to generate recommendation lists for users who have no information at all because there is no preference information about them that could be leveraged, which is the user fully-cold-start problem. In this paper, a review aware cross-domain recommendation algorithm, called RACRec, is proposed to address the fullycold-start problem in the field of product recommendation. Firstly, reviews are dynamically selected by using the adjacency matrix. Secondly, domain-specific preference vectors and domain-shared preference vectors of the cold start user are extracted by a migration model. On the other hand, the product feature vector in the target domain, which is generated from review texts by encoder and decoder, is combined with preference vectors of the cold-start user to make the rating prediction. Experiments on the Amazon datasets reveal that RACRec outperforms the state-of-the-art recommendation algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Related Work a Deep Learning Recommender Systemmentioning

confidence: 99%

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RACRec: Review Aware Cross-Domain Recommendation for Fully-Cold-Start User

et al. 2020

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“…We compare our AGTR model with the following state-ofthe-art methods. SentiRec [14] first encodes each review into a fixed-size vector using CNN and then generates recommendations using vector-encoded reviews.…”

Section: Compared Methodsmentioning

confidence: 99%

AGTR: Adversarial Generation of Target Review for Rating Prediction

Qian

Liang

et al. 2020

Data Sci. Eng.

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Recent years have witnessed a growing trend of utilizing reviews to improve the performance and interpretability of recommender systems. Almost all existing methods learn the latent representations from the user’s and the item’s historical reviews and then combine these two representations for rating prediction. The fatal limitation in these methods is that they are unable to utilize the most predictive review of the target user for the target item since such a review is not available at test time. In this paper, we propose a novel recommendation model, called AGTR, which can generate the unseen target review with adversarial training for rating prediction. To this end, we develop a unified framework to combine the rating tailored generative adversarial nets for synthetic review generation and the neural latent factor module using the generated target review along with historical reviews for rating prediction. Extensive experiments on four real-world datasets demonstrate that our model achieves the state-of-the-art performance in both rating prediction and review generation tasks.

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“…This proposed layer can better capture the interaction between users and items and also learns the importance of each factor to overall ratings. Another model called SentiRec (Hyun et al 2018) leverages the work of Zheng et al (2017) and proposed a modification. SentiRec is guided to incorporate the sentiments of reviews (that was lacking in DeepCoNN) in order to model users and items from review text.…”

Section: Convolutional Neural Network For Rating Predictionmentioning

confidence: 99%

Deep learning techniques for rating prediction: a survey of the state-of-the-art

et al. 2020

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With the growth of online information, varying personalization drifts and volatile behaviors of internet users, recommender systems are effective tools for information filtering to overcome the information overload problem. Recommender systems utilize rating prediction approaches i.e. predicting the rating that a user will give to a particular item, to generate ranked lists of items according to the preferences of each user in order to make personalized recommendations. Although previous recommendation systems are effective in creating attired recommendations, however, they still suffer from different types of challenges such as accuracy, scalability, cold-start, and data sparsity. In the last few years, deep learning has attained substantial interest in various research areas such as computer vision, speech recognition, and natural language processing. Deep learning based approaches are vigorous in not only performance improvement but also to feature representations learning from the scratch. The impact of deep learning is also prevalent, recently validating its efficacy on information retrieval and recommender systems research. In this study, a comprehensive review of deep learning-based rating prediction approaches is provided to help out new researchers interested in the subject. More concretely, the classification of deep learning-based recommendation/rating prediction models is provided and articulated along with an extensive summary of the state-of-the-art. Lastly, new trends are exposited with new perspectives pertaining to this novel and exciting development of the field.

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Review Sentiment-Guided Scalable Deep Recommender System

Cited by 32 publications

References 12 publications

RACRec: Review Aware Cross-Domain Recommendation for Fully-Cold-Start User

RACRec: Review Aware Cross-Domain Recommendation for Fully-Cold-Start User

AGTR: Adversarial Generation of Target Review for Rating Prediction

Deep learning techniques for rating prediction: a survey of the state-of-the-art

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