M. Schwarzer, Corinna Breitinger, M. Schubotz, Norman Meuschke, Bela Gipp
{"title":"Citolytics:一个基于链接的维基百科推荐系统","authors":"M. Schwarzer, Corinna Breitinger, M. Schubotz, Norman Meuschke, Bela Gipp","doi":"10.1145/3109859.3109981","DOIUrl":null,"url":null,"abstract":"We present Citolytics - a novel link-based recommendation system for Wikipedia articles. In a preliminary study, Citolytics achieved promising results compared to the widely used text-based approach of Apache Lucene's MoreLikeThis (MLT). In this demo paper, we describe how we plan to integrate Citolytics into the Wikipedia infrastructure by using Elasticsearch and Apache Flink to serve recommendations for Wikipedia articles. Additionally, we propose a large-scale online evaluation design using the Wikipedia Android app. Working with Wikipedia data has several unique advantages. First, the availability of a very large user sample contributes to statistically significant results. Second, the openness of Wikipedia's architecture allows making our source code and evaluation data public, thus benefiting other researchers. If link-based recommendations show promise in our online evaluation, a deployment of the presented system within Wikipedia would have a far-reaching impact on Wikipedia's more than 30 million users.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Citolytics: A Link-based Recommender System for Wikipedia\",\"authors\":\"M. Schwarzer, Corinna Breitinger, M. Schubotz, Norman Meuschke, Bela Gipp\",\"doi\":\"10.1145/3109859.3109981\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present Citolytics - a novel link-based recommendation system for Wikipedia articles. In a preliminary study, Citolytics achieved promising results compared to the widely used text-based approach of Apache Lucene's MoreLikeThis (MLT). In this demo paper, we describe how we plan to integrate Citolytics into the Wikipedia infrastructure by using Elasticsearch and Apache Flink to serve recommendations for Wikipedia articles. Additionally, we propose a large-scale online evaluation design using the Wikipedia Android app. Working with Wikipedia data has several unique advantages. First, the availability of a very large user sample contributes to statistically significant results. Second, the openness of Wikipedia's architecture allows making our source code and evaluation data public, thus benefiting other researchers. If link-based recommendations show promise in our online evaluation, a deployment of the presented system within Wikipedia would have a far-reaching impact on Wikipedia's more than 30 million users.\",\"PeriodicalId\":417173,\"journal\":{\"name\":\"Proceedings of the Eleventh ACM Conference on Recommender Systems\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Eleventh ACM Conference on Recommender Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3109859.3109981\",\"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 of the Eleventh ACM Conference on Recommender Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3109859.3109981","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Citolytics: A Link-based Recommender System for Wikipedia
We present Citolytics - a novel link-based recommendation system for Wikipedia articles. In a preliminary study, Citolytics achieved promising results compared to the widely used text-based approach of Apache Lucene's MoreLikeThis (MLT). In this demo paper, we describe how we plan to integrate Citolytics into the Wikipedia infrastructure by using Elasticsearch and Apache Flink to serve recommendations for Wikipedia articles. Additionally, we propose a large-scale online evaluation design using the Wikipedia Android app. Working with Wikipedia data has several unique advantages. First, the availability of a very large user sample contributes to statistically significant results. Second, the openness of Wikipedia's architecture allows making our source code and evaluation data public, thus benefiting other researchers. If link-based recommendations show promise in our online evaluation, a deployment of the presented system within Wikipedia would have a far-reaching impact on Wikipedia's more than 30 million users.