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110
A Survey and Comparison of Peer-to-Peer Overlay Network Schemes
- IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
, 2005
"... Over the Internet today, computing and communications environments are significantly more complex and chaotic than classical distributed systems, lacking any centralized organization or hierarchical control. There has been much interest in emerging Peer-to-Peer (P2P) network overlays because they ..."
Abstract
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Cited by 302 (1 self)
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they provide a good substrate for creating large-scale data sharing, content distribution and application-level multicast applications. These P2P networks try to provide a long list of features such as: selection of nearby peers, redundant storage, efficient search/location of data items, data permanence
Bundle Recommendation in eCommerce
"... Recommender system has become an important component in modern eCommerce. Recent research on recommender systems has been mainly concentrating on improving the relevance or profitability of individual recommended items. But in reality, users are usually exposed to a set of items and they may buy mul ..."
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Recommender system has become an important component in modern eCommerce. Recent research on recommender systems has been mainly concentrating on improving the relevance or profitability of individual recommended items. But in reality, users are usually exposed to a set of items and they may buy
A random-walk based scoring algorithm with application to recommender systems for large-scale e-commerce
- Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2006
"... Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present ”ItemRank”, a random–walk based scoring algorithm, whi ..."
Abstract
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Cited by 8 (0 self)
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Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present ”ItemRank”, a random–walk based scoring algorithm
Learning User Profiles from Text in e-Commerce
"... Abstract. Exploring digital collections to find information relevant to a user’s interests is a challenging task. Algorithms designed to solve this relevant information problem base their relevance computations on user profiles in which representations of the users ’ interests are maintained. This p ..."
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Cited by 2 (0 self)
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. This paper presents a new method, based on the classical Rocchio algorithm for text categorization, able to discover user preferences from the analysis of textual descriptions of items in online catalogues of e-commerce Web sites. Experiments have been carried out on a dataset of real users, and results have
Bayesian Multiple Imputation for Large-Scale Categorical Data with Structural Zeros
"... We propose an approach for multiple imputation of items missing at random in large-scale surveys with exclusively categorical variables that have structural zeros. Our approach is to use mixtures of multinomial distributions as imputation engines, accounting for structural zeros by conceiving of the ..."
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Cited by 1 (1 self)
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We propose an approach for multiple imputation of items missing at random in large-scale surveys with exclusively categorical variables that have structural zeros. Our approach is to use mixtures of multinomial distributions as imputation engines, accounting for structural zeros by conceiving
The Phantom of the Marketplace: Searching for New E-Commerce Business Models
- Communications and Strategy, 2nd quarter 2002
, 2002
"... It is widely anticipated that over the next decade Internet-based e-commerce will enable entirely new kinds of business ventures to become substantial new engines of economic growth. The "business model " has emerged as an important concept in this scenario, with much of the current debate ..."
Abstract
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Cited by 8 (0 self)
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debate revolving around the evolution of new business models. As e-commerce can play a role in changing various processes of production and distribution, the argument is that it could result in modification or even replacement of established business models on a very large scale. Problematically
Enhancing Product Search by Best-Selling Prediction in E-Commerce
"... With the rapid growth of E-Commerce on the Internet, online product search service has emerged as a popular and effective paradigm for customers to find desired products and select transactions. Most product search engines today are based on adaptations of relevance models devised for information re ..."
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based on dynamic best-selling prediction in E-Commerce. Specifically, we first develop an effective algorithm to predict the dynamic best-selling, i.e. the volume of sales, for each product item based on its transaction history. By incorporating such bestselling prediction with relevance, we propose a
A Regression-Based Approach for Scaling-Up Personalized Recommender Systems in E-Commerce
- In: ACM WebKDD 2000 Web
, 2000
"... Automated collaborative filtering is one of the key techniques for providing a customization for Ecommerce sites. Various neighbor-based recommendation methods are popular choices for collaborative filtering. However, their latency can be a serious drawback for scaling up to a large number of reques ..."
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Cited by 10 (0 self)
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Automated collaborative filtering is one of the key techniques for providing a customization for Ecommerce sites. Various neighbor-based recommendation methods are popular choices for collaborative filtering. However, their latency can be a serious drawback for scaling up to a large number
Factor Analysis with categorized variables and missing data by design
"... This study compares two approaches for the analysis of large-scale assessment matrix sampling data using limited information factor analysis for categorical variables. In these assessments test takers are required to respond to a small subset of questions and therefore relations between items are no ..."
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This study compares two approaches for the analysis of large-scale assessment matrix sampling data using limited information factor analysis for categorical variables. In these assessments test takers are required to respond to a small subset of questions and therefore relations between items
ClustKNN: a highly scalable hybrid model-& memory-based CF algorithm
- In Proc. of WebKDD-06, KDD Workshop on Web Mining and Web Usage Analysis, at 12 th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining
, 2006
"... Collaborative Filtering (CF)-based recommender systems are indispensable tools to find items of interest from the unmanageable number of available items. Moreover, companies who deploy a CF-based recommender system may be able to increase revenue by drawing customers ’ attention to items that they a ..."
Abstract
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Cited by 31 (1 self)
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that they are likely to buy. However, the sheer number of customers and items typical in e-commerce systems demand specially designed CF algorithms that can gracefully cope with the vast size of the data. Many algorithms proposed thus far, where the principal concern is recommendation quality, may be too expensive
Results 1 - 10
of
110
