2010 IEEE International Conference on Data Mining Workshops最新文献_第7页

Infrequent Purchased Product Recommendation Making Based on User Behaviour and Opinions in E-commerce Sites 基于用户行为和意见的电子商务网站非经常性购买商品推荐

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.116

N. Abdullah, Yue Xu, S. Geva, Jinghong Chen

{"title":"Infrequent Purchased Product Recommendation Making Based on User Behaviour and Opinions in E-commerce Sites","authors":"N. Abdullah, Yue Xu, S. Geva, Jinghong Chen","doi":"10.1109/ICDMW.2010.116","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.116","url":null,"abstract":"Web based commercial recommender systems (RS) can help users to make decisions about which product to purchase from the vast amount of products available on the Internet. Currently, many commercial recommender systems are developed for recommending frequently purchased products where a large amount of explicit ratings or purchase history data is available to predict user preferences. However, for products that are infrequently purchased by users, it is difficult to collect such data and, thus, user profiling becomes a major challenge for recommending these kinds of products. This paper proposes a recommendation approach for infrequently purchased products based on user opinions and navigation data. User opinion data, which is collected from product review data, is used to generate product profiles and user navigation data is used to generate user profiles, both of which are used for recommending products that best satisfy the users’ needs. Experiments conducted on real e-commerce data show that the proposed approach, named, Adaptive Collaborative Filtering (ACF), which utilizes user and product profiles, outperforms the Query Expansion (QE) approach that only utilizes product profiles to recommend products. The ACF also performs better than the Basic Search (BS) approach, which is widely applied by the current e-commerce applications.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"&NA; 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126027959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Ant Colony Optimization Algorithm Based on Immunity Vaccine and Dynamic Pheromone Updating 基于免疫疫苗和信息素动态更新的蚁群优化算法

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.16

Wanjun Liu, Juan Zhang, Junli Liu

引用次数: 1

Network Anomaly Detection Using a Commute Distance Based Approach 基于通勤距离的网络异常检测方法

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.90

N. Khoa, T. Babaie, S. Chawla, Z. Zaidi

引用次数: 9

Interesting Subset Discovery and Its Application on Service Processes 兴趣子集发现及其在服务进程中的应用

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.98

M. Natu, Girish Keshav Palshikar

引用次数: 8

Differential Analysis on Deep Web Data Sources 深层网络数据源的差异分析

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.22

Tantan Liu, Fan Wang, Jiedan Zhu, G. Agrawal

引用次数: 7

FDCluster: Mining Frequent Closed Discriminative Bicluster without Candidate Maintenance in Multiple Microarray Datasets FDCluster:在多个微阵列数据集中挖掘无候选维护的频繁封闭判别双聚类

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.10

Miao Wang, Xuequn Shang, Shaohua Zhang, Zhanhuai Li

引用次数: 22

Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition 蛋白质主链字母的评价:使用预测的局部结构进行折叠识别

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.168

Kyong Jin Shim

引用次数: 0

Efficient Additive Models via the Generalized Lasso 基于广义套索的有效加性模型

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.184

D. Semenovich, Nobuyuki Morioka, A. Sowmya

{"title":"Efficient Additive Models via the Generalized Lasso","authors":"D. Semenovich, Nobuyuki Morioka, A. Sowmya","doi":"10.1109/ICDMW.2010.184","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.184","url":null,"abstract":"We propose a framework for learning generalized additive models at very little additional cost (a small constant) compared to some of the most efficient schemes for learning linear classifiers such as linear SVMs and regularized logistic regression. We achieve this through a simple feature encoding scheme followed by a novel approach to regularization which we term ``generalized lasso''. Addtive models offer an attractive alternative to linear models for many large scale tasks as they have significantly higher predictive power while remaining easily interpretable. Furthermore, our regularizations approach extends to arbitrary graphs, allowing, for example, to explicitly incorporate spatial information or similar priors. Traditional approaches for learning additive models, such as back fitting, do not scale to large datasets. Our new formulation of the resulting optimization problem allows us to investigate the use of recent accelerated gradient algorithms and demonstrate speed comparable to state of the art linear SVM training methods, making additive models suitable for very large problems. In our experiments we find that additive models consistently outperform linear models on various datasets.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129957210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Analysis in Los Angeles Long Beach with Seasonal Time Series Model 基于季节时间序列模型的洛杉矶长滩数据分析

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.93

Weiqiang Wang, Zhendong Niu

引用次数: 2

Sentence-Level and Document-Level Sentiment Mining for Arabic Texts 阿拉伯语文本的句子级和文档级情感挖掘

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.95

N. Farra, Elie Challita, R. A. Assi, Hazem M. Hajj

{"title":"Sentence-Level and Document-Level Sentiment Mining for Arabic Texts","authors":"N. Farra, Elie Challita, R. A. Assi, Hazem M. Hajj","doi":"10.1109/ICDMW.2010.95","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.95","url":null,"abstract":"In this work, we investigate sentiment mining of Arabic text at both the sentence level and the document level. Existing research in Arabic sentiment mining remains very limited. For sentence-level classification, we investigate two approaches. The first is a novel grammatical approach that employs the use of a general structure for the Arabic sentence. The second approach is based on the semantic orientation of words and their corresponding frequencies, to do this we built an interactive learning semantic dictionary which stores the polarities of the roots of different words and identifies new polarities based on these roots. For document-level classification, we use sentences of known classes to classify whole documents, using a novel approach whereby documents are divided dynamically into chunks and classification is based on the semantic contributions of different chunks in the document. This dynamic chunking approach can also be investigated for sentiment mining in other languages. Finally, we propose a hierarchical classification scheme that uses the results of the sentence-level classifier as input to the document-level classifier, an approach which has not been investigated previously for Arabic documents. We also pinpoint the various challenges that are faced by sentiment mining for Arabic texts and propose suggestions for its development. We demonstrate promising results with our sentence-level approach, and our document-level experiments show, with high accuracy, that it is feasible to extract the sentiment of an Arabic document based on the classes of its sentences.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130709289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 186