Domokos M. Kelen, Dániel Berecz, Ferenc Béres, A. Benczúr
{"title":"播放列表延续的高效K-NN算法","authors":"Domokos M. Kelen, Dániel Berecz, Ferenc Béres, A. Benczúr","doi":"10.1145/3267471.3267477","DOIUrl":null,"url":null,"abstract":"We present our solution for the RecSys Challenge 2018, which reached 9th place on the main track leaderboard of the competition. We developed a light-weight playlist-based nearest neighbor method to complete music playlists by using the playlist-track matrix along with track and playlist metadata. Our solution uses a number of domain specific heuristics for improving recommendation quality. One major advantage of our approach is its low computational resource use: our final solution can be computed on a traditional desktop computer within an hour.","PeriodicalId":430663,"journal":{"name":"Proceedings of the ACM Recommender Systems Challenge 2018","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Efficient K-NN for Playlist Continuation\",\"authors\":\"Domokos M. Kelen, Dániel Berecz, Ferenc Béres, A. Benczúr\",\"doi\":\"10.1145/3267471.3267477\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present our solution for the RecSys Challenge 2018, which reached 9th place on the main track leaderboard of the competition. We developed a light-weight playlist-based nearest neighbor method to complete music playlists by using the playlist-track matrix along with track and playlist metadata. Our solution uses a number of domain specific heuristics for improving recommendation quality. One major advantage of our approach is its low computational resource use: our final solution can be computed on a traditional desktop computer within an hour.\",\"PeriodicalId\":430663,\"journal\":{\"name\":\"Proceedings of the ACM Recommender Systems Challenge 2018\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ACM Recommender Systems Challenge 2018\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3267471.3267477\",\"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 ACM Recommender Systems Challenge 2018","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3267471.3267477","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present our solution for the RecSys Challenge 2018, which reached 9th place on the main track leaderboard of the competition. We developed a light-weight playlist-based nearest neighbor method to complete music playlists by using the playlist-track matrix along with track and playlist metadata. Our solution uses a number of domain specific heuristics for improving recommendation quality. One major advantage of our approach is its low computational resource use: our final solution can be computed on a traditional desktop computer within an hour.
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