一个基于网络的电影推荐，使用文本分类

Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003) Pub Date : 2003-10-13 DOI:10.1109/WI.2003.1241277

Harry Mak, I. Koprinska, Josiah Poon

{"title":"一个基于网络的电影推荐，使用文本分类","authors":"Harry Mak, I. Koprinska, Josiah Poon","doi":"10.1109/WI.2003.1241277","DOIUrl":null,"url":null,"abstract":"We present INTIMATE, a Web-based movie recommender that makes suggestions by using text categorization to learn from movie synopses. The performance of various feature representations, feature selectors, feature weighting mechanisms and classifiers is evaluated and discussed. INTIMATE was also compared with a feature-based movie recommender. The results show that the text-based approach outperforms the feature-based if the ratio of the number of user ratings to the vocabulary size is high.","PeriodicalId":403574,"journal":{"name":"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"42","resultStr":"{\"title\":\"INTIMATE: a Web-based movie recommender using text categorization\",\"authors\":\"Harry Mak, I. Koprinska, Josiah Poon\",\"doi\":\"10.1109/WI.2003.1241277\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present INTIMATE, a Web-based movie recommender that makes suggestions by using text categorization to learn from movie synopses. The performance of various feature representations, feature selectors, feature weighting mechanisms and classifiers is evaluated and discussed. INTIMATE was also compared with a feature-based movie recommender. The results show that the text-based approach outperforms the feature-based if the ratio of the number of user ratings to the vocabulary size is high.\",\"PeriodicalId\":403574,\"journal\":{\"name\":\"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"42\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI.2003.1241277\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI.2003.1241277","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 42

摘要

我们介绍了一个基于网络的电影推荐器，它通过使用文本分类从电影大纲中学习来提出建议。对各种特征表示、特征选择器、特征加权机制和分类器的性能进行了评估和讨论。还将《亲密》与基于故事片的电影推荐进行了比较。结果表明，当用户评价数与词汇量之比较高时，基于文本的方法优于基于特征的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

INTIMATE: a Web-based movie recommender using text categorization

We present INTIMATE, a Web-based movie recommender that makes suggestions by using text categorization to learn from movie synopses. The performance of various feature representations, feature selectors, feature weighting mechanisms and classifiers is evaluated and discussed. INTIMATE was also compared with a feature-based movie recommender. The results show that the text-based approach outperforms the feature-based if the ratio of the number of user ratings to the vocabulary size is high.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)

自引率

0.00%

发文量