{"title":"Backward Approach Development for Route Adaptive Mobile Successive Recommendation","authors":"Mrs. S. Nithya, S. Visakh, J. Gollner, C. Ashley","doi":"10.9790/0661-1903032932","DOIUrl":null,"url":null,"abstract":"The Backward Approach Development is the pre-planning requisite for the traveller’s.So they are more exposed to our taxi service in and out. In addition, accurate query results with up-to-date travel times, price etc.we propose a novel dynamic programming based method to solve the mobile sequential recommendation problem with the new algorithm, named UniBic, outperformed all previous biclustering algorithms in terms of commonly used evaluation scenarios except for BicSPAM on narrow biclusters Simultaneously, the process of sequence generation to reduce the search space of the potential sequences effectively. Moreover, our method can handle the problem of optimal route search with a maximum cruising distance or a destination constraint. Experimental results on real and synthetic data sets show that both the pruning ability and the efficiency of our method surpass the state-of-the-art methods. Our techniques can therefore be effectively employed to address the problem of mobile sequential recommendation with many pickup points in real-world applications","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IOSR journal of computer engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9790/0661-1903032932","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Backward Approach Development is the pre-planning requisite for the traveller’s.So they are more exposed to our taxi service in and out. In addition, accurate query results with up-to-date travel times, price etc.we propose a novel dynamic programming based method to solve the mobile sequential recommendation problem with the new algorithm, named UniBic, outperformed all previous biclustering algorithms in terms of commonly used evaluation scenarios except for BicSPAM on narrow biclusters Simultaneously, the process of sequence generation to reduce the search space of the potential sequences effectively. Moreover, our method can handle the problem of optimal route search with a maximum cruising distance or a destination constraint. Experimental results on real and synthetic data sets show that both the pruning ability and the efficiency of our method surpass the state-of-the-art methods. Our techniques can therefore be effectively employed to address the problem of mobile sequential recommendation with many pickup points in real-world applications