Automatic Question Tagging with Deep Neural Networks

Sun, Bo; Zhu, Yunzong; Xiao, Yunhua; Xiao, Rong; Wei, Yungang

doi:10.1109/tlt.2018.2808187

Cited by 32 publications

(13 citation statements)

References 35 publications

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“…Tag recommendation can be roughly divided into nonpersonalized and personalized tag recommendation according to whether the users' personalized preferences are considered when making tag recommendation. Differ from nonpersonalized tag recommendation systems [4]- [7] that provide all users with the same tags for a certain item, personalized tag recommendation systems [2], [3], [8], [9] provide personalized tag recommendation for each user by taking users' tagging preferences into account, which makes personalized tag recommendation more challenging than non-personalized tag recommendation. Due to users' unique personality and habits, different users usually assign different tags for a given item.…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

Chen

Jiang

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Personalized tag recommender systems recommend a set of tags for items based on users' historical behaviors, and play an important role in the collaborative tagging systems. However, traditional personalized tag recommendation methods cannot guarantee that the collaborative signal hidden in the interactions among entities is effectively encoded in the process of learning the representations of entities, resulting in insufficient expressive capacity for characterizing the preferences or attributes of entities. In this paper, we proposed a graph neural networks boosted personalized tag recommendation model, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we consider two types of interaction graph (i.e. the user-tag interaction graph and the item-tag interaction graph) that is derived from the tag assignments. For each interaction graph, we exploit the graph neural networks to capture the collaborative signal that is encoded in the interaction graph and integrate the collaborative signal into the learning of representations of entities by transmitting and assembling the representations of entity neighbors along the interaction graphs. In this way, we explicitly capture the collaborative signal, resulting in rich and meaningful representations of entities. Experimental results on real world datasets show that our proposed graph neural networks boosted personalized tag recommendation model outperforms the traditional tag recommendation models.

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Section: Introductionmentioning

confidence: 99%

Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

Chen

Jiang

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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“…The other methods consider only one of the three pillars. For example, Tag2Word (Wu et al 2016) handles contenttag overlapping under the context of topic models, CNN-RNN (Wang et al 2016) models the tag correlation for image classification, ABC (Gong and Zhang 2016) models the sequential nature of text with CNN, and TLSTM (Li et al 2016b) and PBAM (Sun et al 2018) adopt RNN to model the text. In contrast to these methods, the proposed ITAG takes all the three pillars into consideration.…”

Section: Problem Statementmentioning

confidence: 99%

“…A unique characteristic of RNNs lies in that it stores information circularly by iterating functions. As such, it is particularly suitable for processing sequential input (Li et al 2016a;Sun et al 2018). In our model, we utilize a multilayer RNN structure (i.e., the grid-structure RNN) which includes l layers of RNNs.…”

Section: Modeling Textual Contentmentioning

confidence: 99%

“…The focus of this paper is on the textual content, and we divide existing methods into two steps: text representation and candidate tag generation. Text representation methods include TF-IDF, topic models (Ramage et al 2009;Krestel, Fankhauser, and Nejdl 2009;Seitlinger et al 2013;Wu et al 2016;, and neural networks (Weston, Chopra, and Adams 2014;Li et al 2016a;Gong and Zhang 2016;Li et al 2016b;Sun et al 2018). A recent trend is to apply neural networks such as RNNs to learn the text representations.…”

Section: Related Workmentioning

confidence: 99%

“…https://archive.org/details/stackexchange 5 We do not compare with the latest work(Sun et al 2018) as it requires a hierarchical knowledge graph of tags as input, which is unavailable in our problem setting.• TLSTM(Li et al 2016b). TLSTM combines topic modeling with LSTMs to model the text for tag recommendation.…”

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confidence: 99%

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An Integral Tag Recommendation Model for Textual Content

Tang

Yao

Zhang

et al. 2019

AAAI

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Recommending suitable tags for online textual content is a key building block for better content organization and consumption. In this paper, we identify three pillars that impact the accuracy of tag recommendation: (1) sequential text modeling meaning that the intrinsic sequential ordering as well as different areas of text might have an important implication on the corresponding tag(s) , (2) tag correlation meaning that the tags for a certain piece of textual content are often semantically correlated with each other, and (3) content-tag overlapping meaning that the vocabularies of content and tags are overlapped. However, none of the existing methods consider all these three aspects, leading to a suboptimal tag recommendation. In this paper, we propose an integral model to encode all the three aspects in a coherent encoder-decoder framework. In particular, (1) the encoder models the semantics of the textual content via Recurrent Neural Networks with the attention mechanism, (2) the decoder tackles the tag correlation with a prediction path, and (3) a shared embedding layer and an indicator function across encoder-decoder address the content-tag overlapping. Experimental results on three realworld datasets demonstrate that the proposed method significantly outperforms the existing methods in terms of recommendation accuracy.

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Hierarchical attention model for personalized tag recommendation

Sun

Zhu

Jiang

et al. 2020

Asso for Info Science & Tech

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With the development of Web-based social networks, many personalized tag recommendation approaches based on multi-information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user-item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real-world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model.

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Automatic Question Tagging with Deep Neural Networks

Cited by 32 publications

References 35 publications

Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

An Integral Tag Recommendation Model for Textual Content

Hierarchical attention model for personalized tag recommendation

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