Anr

Chin, Jin Yao; Zhao, Kaiqi; Joty, Shafiq; Cong, Gao

doi:10.1145/3269206.3271810

Cited by 113 publications

(11 citation statements)

References 29 publications

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“…Thus, exploring a sequential architecture comes as a natural and reasonable choice to learn data dynamics, especially when data representations tend to be sparse. Bellini et al (2017), Chae et al (2019), He et al (2019), Hu et al (2019), Jhamb et al (2018), Lee et al (2017Lee et al ( , 2018, Liang et al (2018), Liu et al (2017), Nisha and Mohan (2019), Song et al (2019), Wang, Chen, et al (2019), Wang et al (2020) Convolutional neural network (CNN) 9 Chen, Cai, et al (2019), Da Costa and Dolog (2019), Hyun et al (2018), Liu et al (2017Liu et al ( , 2019, Wang, Chen, et al (2019), Zhang, Cheng, and Ren (2019), Zhang, Yao, et al (2017), Zheng et al (2017) Generative adversarial network (GAN) 3 Chae et al 2019, Lee et al (2017), Wang, Chen, et al (2019) Graph neural network (GNN) 2 Wu, Hong, et al (2019), Zheng et al (2018) Multilayer perceptron (MLP) 20 Bai et al (2017), Cao et al, 2018, C. Chen et al (2020, L. Chen, Zheng, et al (2018), W. Chen, Cai, et al (2019) , Zhou et al (2019) Neural attention 13 (Cao et al (2018), L. Chen, Zheng, et al, 2018, Chin et al, 2018, W. Fan et al (2019, Feng & Zeng, 2019, Jhamb et al (2018…”

Section: Discussionmentioning

confidence: 99%

“…It is worth mentioning the aforementioned approach is quite similar to the review recommendation state-of-the-art TransNet when it comes to considering a proper network for modelling and encoding review information. Chin et al (2018) explore the adoption of a neural attention mechanism for an aspect-based recommendation. The authors propose the Aspect-Based Neural Recommender (ANR), a deep neural architecture that extracts essential information from text reviews and also applies a coattention mechanism to jointly encode user-item interactions to capture aspect-level information from data.…”

Section: Synthesis Of Main Primary Studiesmentioning

confidence: 99%

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Deep learning techniques for recommender systems based on collaborative filtering

2020

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In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well-known CF recommenders suffer from data sparsity, mainly in cold-start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state-of-the-art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low-dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state-of-theart literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non-linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF-based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Synthesis Of Main Primary Studiesmentioning

confidence: 99%

Deep learning techniques for recommender systems based on collaborative filtering

2020

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“…Recent deep neural models such as DeepCoNN (Zheng et al 2017), Dattn (Seo et al 2017), Transnets (Catherine and Cohen 2017) have shown good predictive performance but the single low-dimensional latent representation of each user and item in these models hinders them from capturing fine-grained interactions between users and items. Chin et al (2018) argued that all parts of a review are not equally important e.g. in case of reviews on a movie, some parts may address the plot of the movie where some other part is about user satisfaction.…”

Section: Aspect/topic Modeling Based Recommendationsmentioning

confidence: 99%

“…The rich semantic information in reviews helps us to understand the multi-faceted process behind the tendency of users to rate items. Chin et al (2018) proposed a model called ANR; aspect-based representational learning for both user and items via an attention-based component. The authors claim it is the first paper that proposed an end-to-end neural aspect-based recommendation.…”

Section: Aspect/topic Modeling Based Recommendationsmentioning

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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“…On the other hand, some of the works [12], [17], [54], [64] have explored deep neural networks to perform an in-depth understanding of textual item content and achieved impressive effectiveness by generating more accurate item latent models. In dual-regularized matrix factorization (DRMF) [54], a multilayered neural network model that stacks a convolutional neural network and a gated recurrent neural network is applied to generate independent distributed representations of contents of users and items.…”

Section: B Topic Modeling Based On Contentsmentioning

confidence: 99%

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Wangwatcharakul

Wongthanavasu

2020

IEEE Access

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Recommender systems are efficient tools for online applications; these systems exploit historical user ratings on items to make recommendations of items to users. This paper aims to enhance dynamic collaborative filtering on recommender systems under volatile conditions in which both users' preferences and item properties dynamically change over time. Moreover, existing collaborative filtering models mainly rely on solving data sparsity by adding side information to improve performance. We propose a model to capture the user preference dynamics in the rating matrix by using a joint decomposition method to extract user latent transition patterns and combine latent factors together with the associated topic evolution of review texts by using topic modeling based on the dynamic environment. We evaluate the accuracy on real datasets, and the experimental results show that the model leads to a significant improvement compared with the state-of-the-art dynamic CF models.

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Anr

Cited by 113 publications

References 29 publications

Deep learning techniques for recommender systems based on collaborative filtering

Deep learning techniques for recommender systems based on collaborative filtering

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

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

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