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TYPICALITY-BASED COLLABORATIVE FILTERING RECOMMENDATION  ABSTRACT: Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy and big-error in predictions. In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method […]

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A COCKTAIL APPROACH FOR TRAVEL PACKAGE RECOMMENDATION ABSTRACT: Recent years have witnessed an increased interest in recommender systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to […]

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PRODUCT ASPECT RANKING AND ITS APPLICATIONS ABSTRACT: Numerous consumer reviews of products are now available on the Internet. Consumer reviews contain rich and valuable knowledge for both firms and users. However, the reviews are often disorganized, leading to difficulties in information navigation and knowledge acquisition. This article proposes a product aspect ranking framework, which automatically […]

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LARS*: AN EFFICIENT AND SCALABLE LOCATION-AWARE RECOMMENDER SYSTEM ABSTRACT: This paper proposes LARS*, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items; LARS*, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for […]

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SUPPORTING PRIVACY PROTECTION IN PERSONALIZED WEB SEARCH ABSTRACT: Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. However, evidences show that users’ reluctance to disclose their private information during search has become a major barrier for the wide proliferation of PWS. We study privacy protection […]

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 Data Mining with Big Data ABSTRACT: Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem […]

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TrustedDB:A TRUSTED HARDWARE BASED DATABASE WITH PRIVACY AND DATA CONFIDENTIALITY ABSTRACT: Traditionally, as soon as confidentiality becomes a concern, data is encrypted before outsourcing to a service provider. Any software-based cryptographic constructs then deployed, for server-side query processing on the encrypted data, inherently limit query expressiveness. Here, we introduce TrustedDB, an outsourced database prototype that […]

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SECURE MINING OF ASSOCIATION RULES IN HORIZONTALLY DISTRIBUTED DATABASES ABSTRACT: We propose a protocol for secure mining of association rules in horizontally distributed databases. The current leading protocol is that of Kantarcioglu and Clifton. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al. which is an unsecured […]

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EFFICIENT RANKING ON ENTITY GRAPHS WITH PERSONALIZED RELATIONSHIPS ABSTRACT: Authority flow techniques like Page Rank and Object Rank can provide personalized ranking of typed entity-relationship graphs. There are two main ways to personalize authority flow ranking: Node-based personalization, where authority originates from a set of user-specific nodes; Edge-based personalization, where the importance of different edge […]

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CONTENT CACHING AND SCHEDULING IN WIRELESS NETWORKS WITH ELASTIC AND INELASTIC TRAFFIC ABSTRACT: The rapid growth of wireless content access implies the need for content placement and scheduling at wireless base stations. We study a system under which users are divided into clusters based on their channel conditions, and their requests are represented by different […]

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