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Article
Which noise affects algorithm robustness for learning to rank
When applying learning to rank algorithms in real search applications, noise in human labeled training data becomes an inevitable problem which will affect the performance of the algorithms. Previous work main...
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Chapter and Conference Paper
Bridging Semantic Gap Between App Names: Collective Matrix Factorization for Similar Mobile App Recommendation
With the increase of mobile apps, i.e. applications, it is more and more difficult for users to discover their desired apps. Similar app recommendation, which plays a critical role in the app discovering proce...
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Chapter and Conference Paper
Wake-Sleep Variational Autoencoders for Language Modeling
Variational Autoencoders (VAEs) are known to easily suffer from the KL-vanishing problem when combining with powerful autoregressive models like recurrent neural networks (RNNs), which prohibits their wide app...
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Article
Modeling Temporal Dynamics of Users’ Purchase Behaviors for Next Basket Prediction
Next basket prediction attempts to provide sequential recommendations to users based on a sequence of the user’s previous purchases. Ideally, a good prediction model should be able to explore the personalized ...
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Chapter and Conference Paper
Robust Sequence Embedding for Recommendation
Sequential recommendation is a significant task that predicts the next items given user historical transaction sequences. It is often reduced to a multi-classification task with the historical sequence as the ...
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Chapter and Conference Paper
Learning the Satisfiability of Pseudo-Boolean Problem with Graph Neural Networks
Graph Neural Network (GNN) has shown great power on many practical tasks in the past few years. It is also considered to be a potential technique in bridging the gap between machine learning and symbolic reaso...
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Chapter and Conference Paper
Topological Interpretable Multi-scale Sequential Recommendation
Sequential recommendation attempts to predict next items based on user historical sequences. However, items to be predicted next depend on user’s long, short or mid-term interest. The multi-scale modeling of u...
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Chapter and Conference Paper
Latent Graph Recurrent Network for Document Ranking
BERT based ranking models are emerging for its superior natural language understanding ability. The attention matrix learned through BERT captures all the word relations in the input text. However, neural rank...
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Article
Open AccessDisentangled Graph Recurrent Network for Document Ranking
BERT-based ranking models are emerging for its superior natural language understanding ability. All word relations and representations in the concatenation of query and document are modeled in the self-attenti...
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Chapter and Conference Paper
Unify the Usage of Lexicon in Chinese Named Entity Recognition
Lexicon plays a critical role in Chinese Named Entity Recognition (CNER). The major reason lies in that words in the lexicon, lexicon words for short, are highly related to entity mention boundaries. Most lexi...
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Chapter and Conference Paper
Incorporating Social-Aware User Preference for Video Recommendation
Modeling user interest accurately is crucial to recommendation systems. Existing works capture user interest from historical behaviors. Due to the sparsity and noise in user behavior data, behavior based model...
