Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics最新文献

WIMS 2020: The 10th International Conference on Web Intelligence, Mining and Semantics, Biarritz, France, June 30 - July 3, 2020 WIMS 2020:第10届网络智能、挖掘和语义国际会议，2020年6月30日至7月3日，法国比亚里茨

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-01-01 DOI: 10.1145/3405962

引用次数: 0

Capturing customer context from social media: mapping social media API and CRM profile data 从社交媒体获取客户上下文:映射社交媒体API和CRM配置文件数据

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3117762

Matthias Wittwer, Olaf Reinhold, R. Alt

引用次数: 0

Semantically readable distributed representation learning for social media mining 面向社交媒体挖掘的语义可读分布式表示学习

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106521

Ikuo Keshi, Yumiko Suzuki, Koichiro Yoshino, Satoshi Nakamura

{"title":"Semantically readable distributed representation learning for social media mining","authors":"Ikuo Keshi, Yumiko Suzuki, Koichiro Yoshino, Satoshi Nakamura","doi":"10.1145/3106426.3106521","DOIUrl":"https://doi.org/10.1145/3106426.3106521","url":null,"abstract":"The problem with distributed representations generated by neural networks is that the meaning of the features is difficult to understand. We propose a new method that gives a specific meaning to each node of a hidden layer by introducing a manually created word semantic vector dictionary into the initial weights and by using paragraph vector models. Our experimental results demonstrated that weights obtained based on learning and weights based on the dictionary are more strongly correlated in a closed test and more weakly correlated in an open test, compared with the results of a control test. Additionally, we found that the learned vector are better than the performance of the existing paragraph vector in the evaluation of the sentiment analysis task. Finally, we determined the readability of document embedding in a user test. The definition of readability in this paper is that people can understand the meaning of large weighted features of distributed representations. A total of 52.4% of the top five weighted hidden nodes were related to tweets where one of the paragraph vector models learned the document embedding. Because each hidden node maintains a specific meaning, the proposed method succeeds in improving readability.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85664907","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: 6

Modeling random projection for tensor objects 张量对象的随机投影建模

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106504

Ryohei Yokobayashi, T. Miura

引用次数: 2

A sentiment polarity classifier for regional event reputation analysis 区域事件声誉分析的情感极性分类器

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3109416

Tatsuya Ohbe, Tadachika Ozono, T. Shintani

引用次数: 6

The path to success: a study of user behaviour and success criteria in online communities 成功之路:在线社区用户行为和成功标准的研究

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106469

Erik Aumayr, Conor Hayes

引用次数: 1

UIS-LDA: a user recommendation based on social connections and interests of users in uni-directional social networks us - lda:基于单向社交网络中用户的社交关系和兴趣的用户推荐

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106494

Ke Xu, Y. Cai, Huaqing Min, Xushen Zheng, Haoran Xie, Tak-Lam Wong

{"title":"UIS-LDA: a user recommendation based on social connections and interests of users in uni-directional social networks","authors":"Ke Xu, Y. Cai, Huaqing Min, Xushen Zheng, Haoran Xie, Tak-Lam Wong","doi":"10.1145/3106426.3106494","DOIUrl":"https://doi.org/10.1145/3106426.3106494","url":null,"abstract":"The rapid growth of population has posed a challenge to people for discovering new followees in uni-directional social networks. Intuitively, a user's adoption of others as followees may motivated by her interest as well as social connection. Therefore, it is worth-while to consider both factors at the same time for better recommendations. Previous recommender works on implicit follow or not feedbacks become unqualified, mainly because of the coarse users' preferences inferring, which cannot distinguish whether one follows the other is based on her social connection or individual interest. In this paper, we present a new user recommendation method which is capable of recommending candidate followees who have similar interest and closer social connection relevant to a target user. As its core, a novel topic model namely UIS-LDA is designed to jointly model a user's preferences with respect to the set of latent interest topics and social topics. The experiments using Twitter dataset proves that our proposed method effective in improving the Precision, Conversion Rate F1 score and NDCG.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"9 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91474433","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: 8

Data modeling of smart urban object networks 智慧城市对象网络的数据建模

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3117759

Michael Aleithe, P. Skowron, Bogdan Franczyk, B. Sommer

引用次数: 3

Towards automatic learning content sequence via linked open data 通过链接开放数据实现内容序列的自动学习

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3110322

R. Manrique

引用次数: 4

A novel learning-to-rank based hybrid method for book recommendation 一种基于学习排序的图书推荐混合方法

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106547

Y. Liu, Jiajun Yang

{"title":"A novel learning-to-rank based hybrid method for book recommendation","authors":"Y. Liu, Jiajun Yang","doi":"10.1145/3106426.3106547","DOIUrl":"https://doi.org/10.1145/3106426.3106547","url":null,"abstract":"Recommendation system is able to recommend items that are likely to be preferred by the user. Hybrid recommender systems combine the advantages of the collaborative filtering and content-based filtering for improved recommendation. Hybrid recommendation methods use as many significant factors as possible to generate recommendation, which is practically very functional in real scenarios. However, such method has not been applied to book recommendation yet. Thus, in this paper, we propose a set of novel features which can be categorized into three types: latent features, derived features and content features. These features can be combined to form a new hybrid feature vector containing rating information and content information. Then, we adopted learning-to-rank to use the proposed feature vector as the input for book recommendation. Collaborative Ranking (CR) and Probabilistic Matrix Factorization (PMF) are compared with our proposed method. The experimental results show that the proposed method outperforms CR and PMF. It shows that, on NDCG@1, PMF achieves 0.713818, CR achieves 0.690072 vs. our method achieves 0.742689 which is 4.04% over PMF and 7.62% over CR.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86693356","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: 5