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	1. Personalized Review Recommendation based on Users' Aspect Sentiment
	CHUNLI HUANG, WENJUN JIANG,JIE WU,GUOJUN WANG
	Computer Science and Technology 02 April 2020
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	Abstract:Product reviews play an important role in guiding users' purchase decision-making in e-commerce platforms.However, it is challenging for users to find helpful reviews that meet their preferences and experiences among an overwhelming amount of reviews.While some existing personalized review recommendation models neglect an user's aspect preferences or the user-product interactions for measuring user similarity.Moreover, those works predict review helpfulness at the review-level (a review is taken as a whole); few of them consider the aspect-level.To address the above issues, this paper propose an users' aspect sentiment similarity-based personalized review recommendation model ($A2SPR$), which quantifies review helpfulness and recommends reviews that are customized for each individual.Firstly, the paper analyze users' aspect preferences from reviews and improve user similarity with users' fine-grained sentiment similarity and product relevance.Furthermore, the review helpfulness score is redefined at the aspect level, which indicates the review's reference value for users' purchase decisions. Finally, recommending the top $k$ helpful reviews for individuals based on the review helpfulness score. To validate the performance of the proposed model, eight baselines are developed and compared.Experimental results show that our model performs better than those baselines in both the coverage and precision.
	TO cite this article:CHUNLI HUANG, WENJUN JIANG,JIE WU, et al. Personalized Review Recommendation based on Users' Aspect Sentiment[OL].[ 2 April 2020] http://en.paper.edu.cn/en_releasepaper/content/4751464


	2. Extracting Logical Rules and Minimal Attribute Subset from Confidence Domain
	LiMin Wang
	Computer Science and Technology 29 March 2011
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	Abstract:By evaluating the information quantity implicated in training samples, the SLRFD algorithm proposed in this paper divides the training set into two parts, confidence domain and non-confidence domain. The confidence domain is used to translate training samples into logical rules and assign class labels to testing samples in the semi-supervised learning framework. Functional dependency rules of probability are deduced based on Armstrong's axioms to serve the purpose of semantics-preserving data dimensionality reduction for classification without sacrificing the ability to discern between samples belonging to different classes. The attributes corresponding to missing values are proved to be redundant, thus the computational complexity while modeling will be reduced. Empirical studies on a set of natural domains show that SLRFD has clear advantages with respect to generalization and probabilistic performance.
	TO cite this article:LiMin Wang. Extracting Logical Rules and Minimal Attribute Subset from Confidence Domain[OL].[29 March 2011] http://en.paper.edu.cn/en_releasepaper/content/4419041


	3. The Expected First Hitting Time of a class of Gene Expression Programming
	Du Xin ,Ding Lixin
	Computer Science and Technology 20 July 2010
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	Abstract:This paper studies the time complexity of gene expression programming based on maintaining elitist (ME-GEP). By using the theory of Markov Chain and the technique of Artificial fitness level, the upper and lower bounds of the expected first hitting time(EFHT) of ME-GEP are obtained. Furthermore, the unknown parameter of the upper bound is estimated, which is determined by the parameters of ME-GEP algorithm. As an application of the theoretical results acquired in the paper, the EFHT of ME-GEP for solving the polynomial function modeling problem are analyzed, which verifies the relations between the EFHT and the parameters of ME-GEP algorithm. Finally, our theoretical result is used in directing the parameter design of ME-GEP algorithm. And the experiments of solving the above polynomial function modeling problem verify the correctness of our theoretical result.
	TO cite this article:Du Xin ,Ding Lixin . The Expected First Hitting Time of a class of Gene Expression Programming[OL].[20 July 2010] http://en.paper.edu.cn/en_releasepaper/content/4379501


	4. A New View of Learning Machine
	Maozhi Hu
	Computer Science and Technology 25 November 2005
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	Abstract:for the development of LM.SVM is a new type of LM, which is base on SLT. It is widely used in mode recognize because of its better performance in learning capacity, This paper focuses on the comparison between SVM(a type of LM) and human brain and predicts bravely the future of SVM.
	TO cite this article:Maozhi Hu. A New View of Learning Machine[OL].[25 November 2005] http://en.paper.edu.cn/en_releasepaper/content/3784

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