deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different
            Label for Affect in Tweets

Gao, Zi Yuan; Chen, Chia-Ping

doi:10.18653/v1/s18-1034

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…The main idea of the attention mechanism is intuitively similar to human visual attention, "observing important parts". At present, attention mechanisms have been shown effective in various machine learning tasks such as image / video captioning [6], machine translation [2], and natural language processing [21]. Recently, neural network attention mechanisms have been used in recommendation systems [11].…”

Section: Attention Mechanismmentioning

confidence: 99%

Recommended System: Attentive Neural Collaborative Filtering

Guo

Zhou

2020

IEEE Access

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In recent years, neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation-collaborative filtering-on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of music. When it comes to model the key factor in collaborative filtering-the interaction between users and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. And the collaboration signal hidden in the user-item interaction is not encoded during the embedding process. Therefore, the resulting embedding may not be sufficient to capture the collaborative filtering effect. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general method named ANCF(Attention Neural network Collaborative Filtering). ANCF captures collaborative filtering signals and refines the embedding of users and items according to the structure of the graph. By introducing an attention mechanism, the user vector and the item vector are learned on the user-item interaction graph, neighbor interaction information is aggregated to encode, and the embedding is propagated on the user-item interaction graph. This makes it possible to explicitly inject user-item collaboration signals into the embedding process. Extensive experiments conducted on two real world datasets show that ANCF's recall and ndcg have increased by 30% and 35%, so our proposed ANCF method has been significantly improved over the state-of-the-art method. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.

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Section: Attention Mechanismmentioning

confidence: 99%

Recommended System: Attentive Neural Collaborative Filtering

Guo

Zhou

2020

IEEE Access

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“…Particularly, it equips a neural network to be capable of selecting the most important parts of the target input such as a specific word in a given review or a particular region in an image. This idea has been usefully applied to a number of applications such as computer vision [30], machine translation [31] and natural language processing [32]. More recently neural attention has been exploited for building recommender system [12], [21], [33].…”

Section: Attentive Recommender Systemsmentioning

confidence: 99%

Sentiment-Aware Deep Recommender System With Neural Attention Networks

Da’u

Salim

2019

IEEE Access

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With the advent of web technology, user-generated textual reviews are becoming increasingly accumulated on many e-commerce websites. These reviews contain not only the user comments on different aspects of the products but also the user sentiments associated with the aspects. Although these user sentiments serve as vital side information for improving the performance of recommender systems, most existing approaches ignore to fully exploit them in modeling the fine-grained user-item interaction for improving recommender system performance. Thus, this paper proposes a sentiment-aware deep recommender system with neural attention network (SDRA), which can capture both the aspects of products and the underlying user sentiments associated with the aspects for improving the recommendation system performance. Particularly, a semi-supervised topic model is designed to extract the aspects of the product and the associated sentiment lexicons from the user textual reviews, which are then incorporated into a long short term memory (LSTM) encoder via an interactive neural attention mechanism for better learning of the user and item sentiment-aware representation. Furthermore, a co-attention mechanism is introduced to better model the fine-grained user-item interaction for improving predictive performance. The extensive experiments on different datasets showed that our proposed SDRA model can achieve better performance over the baseline approaches.

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Collaborative Filtering: Graph Neural Network with Attention

Guo

Zhou

2020

Web Information Systems and Applications

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deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets

Cited by 4 publications

References 14 publications

Recommended System: Attentive Neural Collaborative Filtering

Recommended System: Attentive Neural Collaborative Filtering

Sentiment-Aware Deep Recommender System With Neural Attention Networks

Collaborative Filtering: Graph Neural Network with Attention

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