{"title":"Enhancing the Accuracy of Movie Recommendation System Based on Probabilistic Data Structure and Graph Database","authors":"Ashish Sharma, Shalini Batra","doi":"10.1109/ICACC.2015.14","DOIUrl":null,"url":null,"abstract":"User-based Collaborative-filtering (CF) which uses the matrix to store the ratings of the user, is the most frequently used recommender technique, widely used because of its simplicity and efficient performance. Although it is extensively used, one of its major problems is that its performance decreases when the user-item matrix becomes sparse. This paper provides a novel technique to overcome sparsity by the usage of combination of graph data base and with Locality Sensitive Hashing (LSH). Graph database provide the flexibility to developer to design database without performing any normalization and LSH provide the faster way to find the nearest neighbor for the recommendation to user. Paper concludes with comparison of traditional approach with the proposed approach.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACC.2015.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
User-based Collaborative-filtering (CF) which uses the matrix to store the ratings of the user, is the most frequently used recommender technique, widely used because of its simplicity and efficient performance. Although it is extensively used, one of its major problems is that its performance decreases when the user-item matrix becomes sparse. This paper provides a novel technique to overcome sparsity by the usage of combination of graph data base and with Locality Sensitive Hashing (LSH). Graph database provide the flexibility to developer to design database without performing any normalization and LSH provide the faster way to find the nearest neighbor for the recommendation to user. Paper concludes with comparison of traditional approach with the proposed approach.
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