Pub Date : 2018-03-04DOI: 10.15740/HAS/IJCBM/11.1/87-93
Sudhir A. Atwadkar, Miss Umeshwari P. Patil
The E-Commerce market is thriving and poised for robust growth in Asia. There are players who made a good beginning. Their success depends on their understanding of the market and offering various types of features. This paper gives an overview of the future of E-Commerce in India and discusses the future growth segments in India’s E-Commerce. Also find out various factors that would essential for future growth of Indian E-commerce. And represent the various opportunities for retailers, wholesalers, producers and for people. In this paper we found that the Overall E-Commerce will increase exponentially in coming years in the emerging market of India.
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Ranking fraud within the mobile App market refers to deceitful or deceptive activities that have a purpose of bumping up the Apps within the quality list. Indeed, it becomes a lot of and a lot of frequent for App developers to use shady means that, like inflating their Apps’ sales or posting phony App ratings, to commit ranking fraud. Whereas the importance of preventing ranking fraud has been well known, there's restricted understanding and analysis during this space. to the current finish, during this paper, we offer a holistic read of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we have a tendency to initial propose to accurately find the ranking fraud by mining the active periods, specifically leading sessions, of mobile Apps. Such leading sessions are often leveraged for sleuthing the native anomaly rather than international anomaly of App rankings. what is more, we have a tendency to investigate 3 kinds of evidences, i.e., ranking primarily based evidences, rating based evidences and review based evidences, by modeling Apps’ ranking, rating and review behaviors through applied math hypotheses tests .
{"title":"Discovery of Ranking Fraud for Mobile Apps","authors":"Y. Tamboli, P. Satarkar","doi":"10.21090/ijaerd.021140","DOIUrl":"https://doi.org/10.21090/ijaerd.021140","url":null,"abstract":"Ranking fraud within the mobile App market refers to deceitful or deceptive activities that have a purpose of bumping up the Apps within the quality list. Indeed, it becomes a lot of and a lot of frequent for App developers to use shady means that, like inflating their Apps’ sales or posting phony App ratings, to commit ranking fraud. Whereas the importance of preventing ranking fraud has been well known, there's restricted understanding and analysis during this space. to the current finish, during this paper, we offer a holistic read of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we have a tendency to initial propose to accurately find the ranking fraud by mining the active periods, specifically leading sessions, of mobile Apps. Such leading sessions are often leveraged for sleuthing the native anomaly rather than international anomaly of App rankings. what is more, we have a tendency to investigate 3 kinds of evidences, i.e., ranking primarily based evidences, rating based evidences and review based evidences, by modeling Apps’ ranking, rating and review behaviors through applied math hypotheses tests .","PeriodicalId":184465,"journal":{"name":"International Research Journal of Multidisciplinary Studies","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116749403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
TWENTY years past, folks generally created friends with others who live or work near themselves, like neighbors or colleagues. we have a tendency to decision friends created through this ancient fashion as G-friends, that stands for geographical location-based friends as a result of they're influenced by the geographical distances between one another. With the speedy advances in social networks, services like Facebook, Twitter and Google+ have provided us revolutionary ways in which of creating friends.According to Facebook statistics, a user has a mean of one hundred thirty friends, maybe larger than the other time in history. One challenge with existing social networking services is a way to suggest a good or reliable friend to a user. Most of them rely on pre-existing user relationships to choose friend candidates. for instance, Facebook depends on a social link analysis among those that already share common friends and recommends users as potential friends.Unfortunately, this approach might not be the foremost applicable supported recent social science findings. according to these studies, the principles to group individuals along include: 1) habits or life style; 2) attitudes; 3) tastes; 4) ethical standards; 5) economic level; and 6) individuals they already know. life styles are typically closely correlate with daily routines and activities. Therefore, if we tend to may gather data on users’ daily routines and activities, we are able to exploit rule #1 and suggest friends to individuals supported their similar life styles. This recommendation mechanism may be deployed as a standalone app on smartphones for existing social network frameworks.
{"title":"Friendbook: A Semantic-Based Friend Recommendation System for Social Networks","authors":"Rohan S. Kulkarni, V. D. Jadhav","doi":"10.21090/ijaerd.0305110","DOIUrl":"https://doi.org/10.21090/ijaerd.0305110","url":null,"abstract":"TWENTY years past, folks generally created friends with others who live or work near themselves, like neighbors or colleagues. we have a tendency to decision friends created through this ancient fashion as G-friends, that stands for geographical location-based friends as a result of they're influenced by the geographical distances between one another. With the speedy advances in social networks, services like Facebook, Twitter and Google+ have provided us revolutionary ways in which of creating friends.According to Facebook statistics, a user has a mean of one hundred thirty friends, maybe larger than the other time in history. One challenge with existing social networking services is a way to suggest a good or reliable friend to a user. Most of them rely on pre-existing user relationships to choose friend candidates. for instance, Facebook depends on a social link analysis among those that already share common friends and recommends users as potential friends.Unfortunately, this approach might not be the foremost applicable supported recent social science findings. according to these studies, the principles to group individuals along include: 1) habits or life style; 2) attitudes; 3) tastes; 4) ethical standards; 5) economic level; and 6) individuals they already know. life styles are typically closely correlate with daily routines and activities. Therefore, if we tend to may gather data on users’ daily routines and activities, we are able to exploit rule #1 and suggest friends to individuals supported their similar life styles. This recommendation mechanism may be deployed as a standalone app on smartphones for existing social network frameworks.","PeriodicalId":184465,"journal":{"name":"International Research Journal of Multidisciplinary Studies","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114744559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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