Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture

Loyola, P.; Liu, Chen; Hirate, Yu

doi:10.1145/3109859.3109917

Cited by 58 publications

(30 citation statements)

References 13 publications

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“…Given the classical collaborative filtering scenario with user-item interaction behavior, NAIS extended the classical item based recommendation models by distinguishing the importance of different historical items in a user profile [19]. With users' temporal behavior, the attention networks were proposed to learn which historical behavior is more important for the user's current temporal decision [31], [32]. A lot of attention based recommendation models have been developed to better exploit the auxiliary information to improve recommendation performance.…”

Section: Related Workmentioning

confidence: 99%

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A Hierarchical Attention Model for Social Contextual Image Recommendation

Chen

Hong

et al. 2020

IEEE Trans. Knowl. Data Eng.

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Image based social networks are among the most popular social networking services in recent years. With tremendous images uploaded everyday, understanding users' preferences on user-generated images and making recommendations have become an urgent need. In fact, many hybrid models have been proposed to fuse various kinds of side information (e.g., image visual representation, social network) and user-item historical behavior for enhancing recommendation performance. However, due to the unique characteristics of the user generated images in social image platforms, the previous studies failed to capture the complex aspects that influence users' preferences in a unified framework. Moreover, most of these hybrid models relied on predefined weights in combining different kinds of information, which usually resulted in sub-optimal recommendation performance. To this end, in this paper, we develop a hierarchical attention model for social contextual image recommendation. In addition to basic latent user interest modeling in the popular matrix factorization based recommendation, we identify three key aspects (i.e., upload history, social influence, and owner admiration) that affect each user's latent preferences, where each aspect summarizes a contextual factor from the complex relationships between users and images. After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users' latent interests with the identified key aspects. Specifically, by taking embeddings from state-of-the-art deep learning models that are tailored for each kind of data, the hierarchical attention network could learn to attend differently to more or less content. Finally, extensive experimental results on real-world datasets clearly show the superiority of our proposed model. !

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Section: Related Workmentioning

confidence: 99%

“…In the social embedding process with Deepwalk [37], we set the parameters as: the window size w = 10 and walks per vertex ρ = 80. The social embedding size d is set in the range [32,64,128]. We find when d = 128, the social embedding reaches the best performance.…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Hierarchical Attention Model for Social Contextual Image Recommendation

Chen

Hong

et al. 2020

IEEE Trans. Knowl. Data Eng.

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“…Clearly sessions without a search belong to informational (browsing) or transactional (buying) sessions type [90]. When comparing search and nonsearch sessions, researchers report that 46.7% of actions belongs to the user transactions (e.g., mailing), 19.9% to the Web browsing (e.g., news reading), 16.3% to facts searching (e.g., looking for relevant statements), 13.5% to information gathering (e.g., looking for bus departures) and 1.7% to other non classified actions [74].…”

Section: Session Intent Identificationmentioning

confidence: 99%

Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Kassak

Kompan

Bieliková

2018

JWE

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User behaviour in data intensive applications such as the Web-based applications represents a complex set of actions influenced by plenty of factors. Thanks to this complexity, it is extremely hard for human to be able to understand all its aspects. Despite of this, by observing user actions from multiple views, we are able to extract and to model typical behaviour and its deviations on the Web. The website itself, together with transaction server logs, includes information about the site structure, content and about the actual user actions (clicks) within the site. User actions logically reflect the behaviour, while other sources indicate his/her context. Combination of these data sources allows to model the typical user behaviour and his/her preferences. The longterm behaviour describes relatively stable user preferences based on extensive user history. As the Web has become more and more dynamic, modelling user behaviour from the long-term perspective does not satisfy requirements of current Web based applications. On the other side, the short-term behaviour describes current user activity and his/her actual intent. However, this source of information is often noisy. To address these shortcomings the state-ofthe-art combines both perspectives, which allows to meaningful and timely modelling of user behaviour. In this paper, we provide a comprehensive survey

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“…The fusion of deep learning-based models and the attention mechanism can help models emphasize informative features and suppress useless ones. At present, attention has been widely applied in speech recognition [28], answer selection [29], and session prediction [30]. Although the use of attention mechanism is not uncommon in speech enhancement, there are three reasons why we think it can play a role: First, in a noisy environment, the human auditory system can selectively focus on speech while suppressing noise through the attention mechanism [31].…”

Section: Introductionmentioning

confidence: 99%

Combining Multi-Perspective Attention Mechanism With Convolutional Networks for Monaural Speech Enhancement

Lan

Lyu

et al. 2020

IEEE Access

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The redundant convolutional encoder-decoder network has been proven useful in speech enhancement tasks. This network can capture the localized time-frequency details of speech signals through the fully convolutional network structure and the feature selection capability that results from the encoderdecoder mechanism. However, extracting informative features, which we regard as important for the representational capability of speech enhancement models, is not considered explicitly. To solve this problem, we introduce the attention mechanism into the convolutional encoder-decoder model to explicitly emphasize useful information from three aspects, namely, channel, space, and concurrent space-and-channel. Furthermore, the attention operation is specifically achieved through the squeeze-and-excitation mechanism and its variants. The model can adaptively emphasize valuable information and suppress useless ones by assigning weights from different perspectives according to global information, thereby improving its representational capability. Experimental results show that the proposed attention mechanisms can employ a small fraction of parameters to effectively improve the performance of CNN-based models compared with their normal versions, and generalize well to unseen noises, signal-to-noise ratios (SNR) and speakers. Among these mechanisms, the concurrent space-channel-wise attention exhibits the most significant improvement. And when comparing with the state-of-the-art, they can produce comparable or better results. We also integrate the proposed attention mechanisms with other convolutional neural network (CNN)-based models and gain performance. Moreover, we visualize the enhancement results to show the effect of the attention mechanisms more clearly.

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Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture

Cited by 58 publications

References 13 publications

A Hierarchical Attention Model for Social Contextual Image Recommendation

A Hierarchical Attention Model for Social Contextual Image Recommendation

Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Combining Multi-Perspective Attention Mechanism With Convolutional Networks for Monaural Speech Enhancement

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