{"title":"Restaurant recommender system based on psychographic and demographic factors in mobile environment","authors":"R. Katarya, O. Verma","doi":"10.1109/ICGCIOT.2015.7380592","DOIUrl":null,"url":null,"abstract":"Today the number of smart phone users is approximately 1.6 billion and with drastic improvement in internet technology, the way information is accessed and used is changed completely. Recommendation systems filter and recommend only relevant data to the user using different filtering techniques. Restaurant recommendation is one of the latest research area which requires further effort. In this paper, a new model is introduced for the restaurant recommendation which uses first psychographic attributes where lifestyle, interest and personality of an individual can be predicted based on mobile usage pattern, second demographic attributes such as age, gender etc. and third current location. We have verified over results using standard statistical metrics like root mean square or variance.","PeriodicalId":400178,"journal":{"name":"2015 International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Green Computing and Internet of Things (ICGCIoT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICGCIOT.2015.7380592","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
Today the number of smart phone users is approximately 1.6 billion and with drastic improvement in internet technology, the way information is accessed and used is changed completely. Recommendation systems filter and recommend only relevant data to the user using different filtering techniques. Restaurant recommendation is one of the latest research area which requires further effort. In this paper, a new model is introduced for the restaurant recommendation which uses first psychographic attributes where lifestyle, interest and personality of an individual can be predicted based on mobile usage pattern, second demographic attributes such as age, gender etc. and third current location. We have verified over results using standard statistical metrics like root mean square or variance.