{"title":"推荐系统——电子商务时代的需要","authors":"Nayana Vaidya, A. Khachane","doi":"10.1109/ICCMC.2017.8282616","DOIUrl":null,"url":null,"abstract":"Recommendation System belongs to the class of Information Retrieval, Data Mining and Machine Learning. Recommender systems play a major role in today's ecommerce industry. Recommender systems recommend items to users such as books, movies, videos, electronic products and many other products in general. Recommender systems help the users to get personalized recommendations, helps users to take correct decisions in their online transactions, increase sales and redefine the users web browsing experience, retain the customers, enhance their shopping experience. Information overload problem is solved by search engines, but they do not provide personalization of data. Recommendation engines provide personalization. There are different type of recommender systems such as content-based, collaborative filtering, hybrid recommender system, demographic and keyword based recommender system. Variety of algorithms are used by various researchers in each type of recommendation system. Lot of work has been done on this topic, still it is a very favourite topic among data scientists. It also comes under the domain of data Science.","PeriodicalId":163288,"journal":{"name":"2017 International Conference on Computing Methodologies and Communication (ICCMC)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Recommender systems-the need of the ecommerce ERA\",\"authors\":\"Nayana Vaidya, A. Khachane\",\"doi\":\"10.1109/ICCMC.2017.8282616\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recommendation System belongs to the class of Information Retrieval, Data Mining and Machine Learning. Recommender systems play a major role in today's ecommerce industry. Recommender systems recommend items to users such as books, movies, videos, electronic products and many other products in general. Recommender systems help the users to get personalized recommendations, helps users to take correct decisions in their online transactions, increase sales and redefine the users web browsing experience, retain the customers, enhance their shopping experience. Information overload problem is solved by search engines, but they do not provide personalization of data. Recommendation engines provide personalization. There are different type of recommender systems such as content-based, collaborative filtering, hybrid recommender system, demographic and keyword based recommender system. Variety of algorithms are used by various researchers in each type of recommendation system. Lot of work has been done on this topic, still it is a very favourite topic among data scientists. It also comes under the domain of data Science.\",\"PeriodicalId\":163288,\"journal\":{\"name\":\"2017 International Conference on Computing Methodologies and Communication (ICCMC)\",\"volume\":\"109 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Computing Methodologies and Communication (ICCMC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCMC.2017.8282616\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Computing Methodologies and Communication (ICCMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCMC.2017.8282616","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recommendation System belongs to the class of Information Retrieval, Data Mining and Machine Learning. Recommender systems play a major role in today's ecommerce industry. Recommender systems recommend items to users such as books, movies, videos, electronic products and many other products in general. Recommender systems help the users to get personalized recommendations, helps users to take correct decisions in their online transactions, increase sales and redefine the users web browsing experience, retain the customers, enhance their shopping experience. Information overload problem is solved by search engines, but they do not provide personalization of data. Recommendation engines provide personalization. There are different type of recommender systems such as content-based, collaborative filtering, hybrid recommender system, demographic and keyword based recommender system. Variety of algorithms are used by various researchers in each type of recommendation system. Lot of work has been done on this topic, still it is a very favourite topic among data scientists. It also comes under the domain of data Science.