Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion

Song, Jun; Xiao, Jun; Wu, Fei; Wu, Haimeng; Zhang, Tong; Zhang, Zhongfei Mark; Zhu, Wenwu

doi:10.1109/tkde.2017.2700392

Cited by 23 publications

(8 citation statements)

References 26 publications

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“…An attention [41], [42] is intuited from visual attentions of human beings (incline to be attracted by more important parts of a target object). Attention is widely used in many fields, including object detection [43], [44], prediction [45], query suggestion [46], and recommendation [4]. In brief, attention can be used to increase the interpretability and adaptivity of complex models such as neural networks by calculating the weights of different data/information automatically.…”

Section: Attention Mechanismmentioning

confidence: 99%

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Wang

Zhang

Xiang

et al. 2022

IEEE Trans. Cybern.

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Recommender systems are important approaches for dealing with the information overload problem in big data era, and various kinds of auxiliary information, including time and sequential information, can help to improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model named Multivariate Hawkes Process Embedding with attention (MHPE-a), which combines a temporal point process with attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform accurate sequential recommendation to satisfy target users' realtime requirement based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.

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Section: Attention Mechanismmentioning

confidence: 99%

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Wang

Zhang

Xiang

et al. 2022

IEEE Trans. Cybern.

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“…We use attentive Bidirectional-Gated Recurrent Unit (Bi-GRU) [22] to learn the hidden representation. Gated Recurrent Unit (GRU) [22] is an Recurrent Neural Network based model that uses two gates z t and r t to control how information is updated to the state, where t stands for time, or more specifically, the order of words in our context. Bi-GRU considers both directions of a sentence for the better word context.…”

Section: B Feature Representation Learningmentioning

confidence: 99%

Dual-stream Self-Attentive Random Forest for False Information Detection

Dong

Yao

Wang

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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The prevalence of online social media facilitates massive knowledge acquisition and sharing throughout the Web. Meanwhile, it inevitably poses the risk of generating and disseminating false information by both benign and malicious users. Despite there has been considerable research on false information detection from both the opinion-based and factbased perspectives, they mostly focus on tailored solutions for a particular domain and carry out limited work on leveraging multi-faceted clues such as textual cues, behavioral trails, and relational connection. We propose a novel dual-stream attentive random forest that is capable of selecting clues of discriminative information from individuals, collective information (e.g., texts), and correlations of entities (e.g., social interactions) adaptively. In particular, we use an interpretive attention model for learning textual contents. The model treats the important and unimportant content differently when constructing the textual representation and employs a multilayer perceptron to capture the hidden complex relationships among features of side information. We further propose a unified framework for leveraging the above clues, where we use attentive forests to provide probabilistic distribution as predictions over the two learned representations, which are then leveraged to make a better estimation. We conduct extensive experiments on three real-world benchmark datasets for fake news and fake review detection. The results show our approach outperforms multiple baselines in the accuracy of detecting false information.

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“…We utilize recurrent neural network (RNN) for sequential user modeling, as illustrated in Figure 3. Leveraging RNN for sequential modeling and time-series prediction has been widely applied in information retrieval systems [19,26,33]. Note that our methodology aims at final conversion estimation rather than sequential prediction for click at each touch point.…”

Section: Sequential Modelingmentioning

confidence: 99%

“…These methods are all based on the assumption that the user conversion would be driven by the individual advertising touch point of positive influence [24,37], which may not be realistic for the user journey (conversion funnel). In fact, sequential patterns within the user browsing behavior are of great value for response prediction or decision making in many fields such as recommender systems [23], information retrieval [26] and search advertising [36].…”

Section: Introductionmentioning

confidence: 99%

Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

Ren

Zhang

et al. 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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In online advertising, the Internet users may be exposed to a sequence of different ad campaigns, i.e., display ads, search, or referrals from multiple channels, before led up to any final sales conversion and transaction. For both campaigners and publishers, it is fundamentally critical to estimate the contribution from ad campaign touch-points during the customer journey (conversion funnel) and assign the right credit to the right ad exposure accordingly. However, the existing research on the multi-touch attribution problem lacks a principled way of utilizing the users' pre-conversion actions (i.e., clicks), and quite often fails to model the sequential patterns among the touch points from a user's behavior data. To make it worse, the current industry practice is merely employing a set of arbitrary rules as the attribution model, e.g., the popular last-touch model assigns 100% credit to the final touchpoint regardless of actual attributions. In this paper, we propose a Dual-attention Recurrent Neural Network (DARNN) for the multitouch attribution problem. It learns the attribution values through an attention mechanism directly from the conversion estimation objective. To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation. To quantitatively benchmark attribution models, we also propose a novel yet practical attribution evaluation scheme through the proxy of budget allocation (under the estimated attributions) over ad channels. The experimental results on two real datasets demonstrate the significant performance gains of our attribution model against the state of the art.

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Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion

Cited by 23 publications

References 26 publications

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Dual-stream Self-Attentive Random Forest for False Information Detection

Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

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