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Bayesian dual neural networks for recommendation 被引量:3
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作者 Jia HE Fuzhen ZHUANG +2 位作者 Yanchi LIU Qing HE Fen LIN 《Frontiers of Computer Science》 SCIE EI CSCD 2019年第6期1255-1265,共11页
Most traditional collaborative filtering(CF)methods only use the user-item rating matrix to make recommendations,which usually suffer from cold-start and sparsity problems.To address these problems,on the one hand,som... Most traditional collaborative filtering(CF)methods only use the user-item rating matrix to make recommendations,which usually suffer from cold-start and sparsity problems.To address these problems,on the one hand,some CF methods are proposed to incorporate auxiliary information such as user/item profiles;on the other hand,deep neural networks,which have powerful ability in learning effective representations,have achieved great success in recommender systems.However,these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the weights.Therefore,they maybe lack of calibrated probabilistic predictions and make overly confident decisions.To this end,we propose a new Bayesian dual neural network framework,named BDNet,to incorporate auxiliary information for recommendation.Specifically,we design two neural networks,one is to learn a common low dimensional space for users and items from the rating matrix,and another one is to project the attributes of users and items into another shared latent space.After that,the outputs of these two neural networks are combined to produce the final prediction.Furthermore,we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic predictions.Extensive experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors. 展开更多
关键词 collaborative filtering Bayesian neural network hybrid recommendation algorithm
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Rich-text document styling restoration via reinforcement learning 被引量:1
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作者 Hongwei LI Yingpeng HU +2 位作者 Yixuan CAO Ganbin ZHOU Ping LUO 《Frontiers of Computer Science》 SCIE EI CSCD 2021年第4期93-103,共11页
Richly formatted documents,such as financial disclosures,scientific articles,government regulations,widely exist on Web.However,since most of these documents are only for public reading,the styling information inside ... Richly formatted documents,such as financial disclosures,scientific articles,government regulations,widely exist on Web.However,since most of these documents are only for public reading,the styling information inside them is usually missing,making them improper or even burdensome to be displayed and edited in different formats and platforms.In this study we formulate the task of document styling restoration as an optimization problem,which aims to identify the styling settings on the document elements,e.g.,lines,table cells,text,so that rendering with the output styling settings results in a document,where each element inside it holds the(closely)exact position with the one in the original document.Considering that each styling setting is a decision,this problem can be transformed as a multi-step decision-making task over all the document elements,and then be solved by reinforcement learning.Specifically,Monte-Carlo Tree Search(MCTS)is leveraged to explore the different styling settings,and the policy function is learnt under the supervision of the delayed rewards.As a case study,we restore the styling information inside tables,where structural and functional data in the documents are usually presented.Experiment shows that,our best reinforcement method successfully restores the stylings in 87.65%of the tables,with 25.75%absolute improvement over the greedymethod.We also discuss the tradeoff between the inference time and restoration success rate,and argue that although the reinforcement methods cannot be used in real-time scenarios,it is suitable for the offline tasks with high-quality requirement.Finally,this model has been applied in a PDF parser to support cross-format display. 展开更多
关键词 styling restoration monte-carlo tree search reinforcement learning richly formatted documents TABLES
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Sememe knowledge computation:a review of recent advances in application and expansion of sememe knowledge bases 被引量:1
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作者 Fanchao QI Ruobing XIE +2 位作者 Yuan ZANG Zhiyuan LIU Maosong SUN 《Frontiers of Computer Science》 SCIE EI CSCD 2021年第5期13-23,共11页
A sememe is defined as the minimum semantic unit of languages in linguistics.Sememe knowledge bases are built by manually annotating sememes for words and phrases.HowNet is the most well-known sememe knowledge base.It... A sememe is defined as the minimum semantic unit of languages in linguistics.Sememe knowledge bases are built by manually annotating sememes for words and phrases.HowNet is the most well-known sememe knowledge base.It has been extensively utilized in many natural language processing tasks in the era of statistical natural language processing and proven to be effective and helpful to understanding and using languages.In the era of deep learning,although data are thought to be of vital importance,there are some studies working on incorporating sememe knowledge bases like HowNet into neural network models to enhance system performance.Some successful attempts have been made in the tasks including word representation learning,language modeling,semantic composition,etc.In addition,considering the high cost of manual annotation and update for sememe knowledge bases,some work has tried to use machine learning methods to automatically predict sememes for words and phrases to expand sememe knowledge bases.Besides,some studies try to extend HowNet to other languages by automatically predicting sememes for words and phrases in a new language.In this paper,we summarize recent studies on application and expansion of sememe knowledge bases and point out some future directions of research on sememes. 展开更多
关键词 natural language process SEMANTICS knowledge base SEMEME HOWNET
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