MPM '12Pub Date : 2012-04-10DOI: 10.1145/2181196.2181201
C. Elsmore, Anil Madhavapeddy, I. Leslie, Amir Chaudhry
{"title":"Confidential carbon commuting: exploring a privacy-sensitive architecture for incentivising 'greener' commuting","authors":"C. Elsmore, Anil Madhavapeddy, I. Leslie, Amir Chaudhry","doi":"10.1145/2181196.2181201","DOIUrl":"https://doi.org/10.1145/2181196.2181201","url":null,"abstract":"We discuss the problem of building a user-acceptable infrastructure for a large organisation that wishes to measure its employees' travel-to-work carbon footprint, based on the gathering of high resolution geolocation data on employees in a privacy-sensitive manner. This motivated the construction of a distributed system of personal containers in which individuals record fine-grained location information into a private data-store which they own, and from which they can trade portions of data to the organisation in return for specific benefits. This framework can be extended to gather a wide variety of personal data and facilitates the transformation of private information into a public good, with minimal and assessable loss of individual privacy.\u0000 This is currently a work in progress. We report on the hardware, software and social aspects of piloting this scheme on the University of Cambridge's experimental cloud service, as well as contrasting it to a traditional centralised model.","PeriodicalId":176268,"journal":{"name":"MPM '12","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131474597","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}
MPM '12Pub Date : 2012-04-10DOI: 10.1145/2181196.2181203
Maryam Fatemi, L. Tokarchuk
{"title":"An empirical study on IMDb and its communities based on the network of co-reviewers","authors":"Maryam Fatemi, L. Tokarchuk","doi":"10.1145/2181196.2181203","DOIUrl":"https://doi.org/10.1145/2181196.2181203","url":null,"abstract":"The advent of business oriented and social networking sites on the Internet have seen a huge increase in number of people using them in recent years. With the expansion of Web 2.0, new types of websites have emerged such as online social networks, blogs and wikis. Their popularity has resulted in exponential growth of information on the web and interactions overload thus making it harder to access useful or relevant information. Recommender systems are one of the applications employed to address this problem by filtering relevant information and enhancing user experience. They traditionally use either the content of items of the websites (content-filtering recommender systems) or the collaboration between the users and items such as rating (collaborative-filtering recommender systems) or a combination of them (hybrid recommender systems). However due to the nature of data they use, they all have one or more weaknesses such as cold start, sparsity of data, scalability problems and overspecialised recommendation. Social networks and other similar websites have new types of data which can be used in recommender systems thus have the potential to overcome these shortcomings. However without a good understanding of the properties and structure of these online social websites, the applications can not be accurate. This paper presents an empirical measurement study of the properties and structure of one such social websites. It examines an online movie database, and the interactions between reviewers and attempts to construct a social network graph based on the network of reviewers. The resulting network is confirmed as the power-law, small-world and scale-free. It identifies the highly connected clusters and shows that the content of these subgroups are diversified and not limited to similar tags. Finally the implication of these finding is discussed in order to enhance current recommender systems enabling them to provide diverse results while overcome their shortcomings.","PeriodicalId":176268,"journal":{"name":"MPM '12","volume":"2023 315","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131487026","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}