A Faster Algorithm to Build New Users Similarity List in Neighbourhood-based Collaborative Filtering

Neighbourhood-based Collaborative Filtering (CF) has been applied in the industry for several decades, because of the easy implementation and high recommendation accuracy. As the core of neighbourhood-based CF, the task of dynamically maintaining users' similarity list is challenged by cold-start problem and scalability problem. Recently, several methods are presented on solving the two problems. However, these methods applied an $O(n^2)$ algorithm to compute the similarity list in a special case, where the new users, with enough recommendation data, have the same rating list. To address the problem of large computational cost caused by the special case, we design a faster ($O(\frac{1}{125}n^2)$) algorithm, TwinSearch Algorithm, to avoid computing and sorting the similarity list for the new users repeatedly to save the computational resources. Both theoretical and experimental results show that the TwinSearch Algorithm achieves better running time than the traditional method

An Accuracy-Assured Privacy-Preserving Recommender System for Internet Commerce

Recommender systems, tool for predicting users' potential preferences by computing history data and users' interests, show an increasing importance in various Internet applications such as online shopping. As a well-known recommendation method, neighbourhood-based collaborative filtering has attracted considerable attention recently. The risk of revealing users' private information during the process of filtering has attracted noticeable research interests. Among the current solutions, the probabilistic techniques have shown a powerful privacy preserving effect. When facing $k$ Nearest Neighbour attack, all the existing methods provide no data utility guarantee, for the introduction of global randomness. In this paper, to overcome the problem of recommendation accuracy loss, we propose a novel approach, Partitioned Probabilistic Neighbour Selection, to ensure a required prediction accuracy while maintaining high security against $k$NN attack. We define the sum of $k$ neighbours' similarity as the accuracy metric alpha, the number of user partitions, across which we select the $k$ neighbours, as the security metric beta. We generalise the $k$ Nearest Neighbour attack to beta k Nearest Neighbours attack. Differing from the existing approach that selects neighbours across the entire candidate list randomly, our method selects neighbours from each exclusive partition of size $k$ with a decreasing probability. Theoretical and experimental analysis show that to provide an accuracy-assured recommendation, our Partitioned Probabilistic Neighbour Selection method yields a better trade-off between the recommendation accuracy and system security.Comment: replacement for the previous versio

Pure Exporter: Theory and Evidence

This paper provides the first evidence about pure exporters (i.e., firms exporting all of their output to the foreign market) -- a phenomenon overlooked and cannot be explained in the existing literature. It then offers a generalized model of Melitz (2003) for examining the existence and behavior of pure exporters. In particular, pure exporters arise when the export market is sufficiently large -- a situation more likely to hold in developing countries as opposed to large developed countries; and their productivity levels are above those of non-exporters, but below those of firms having both domestic sales and export. These theoretical predictions are borne out in a data of Chinese manufacturing firms for the period of 1998-2005.Pure Exporter, Firm Heterogeneity, Exporting Behavior

Contract Enforcement and Family Control of Business: Evidence from China

Family control of business is prevalent in developing economies, and one of the leading theories suggests that it is a response to weak contract enforcement in such economies. In this paper, we investigate the impacts of contract enforcement on the degree of family control of business using a sample of China's private enterprises. It is found that weaker contract enforcement is associated with the higher degree of family control of business. Our results are robust to the control for omitted variables and reserve causality issues, to the adjustment for the sample attrition bias, to the use of a sub-sample, and to the inclusion of other explanations for the family control of business.Family Control of Business, Contract Enforcement, China's Private Enterprises

Determinants of Entrepreneurial Activities in China

The institutional environment – including protection of private properties and contract enforcement – has been rather unfavorable for the emergence and development of China’s private enterprises. This is in sharp contrast to the case of the developed economies where the institutional environment is conductive to the entrepreneurial activities and only the personal attributes of would-be entrepreneurs determine their entrepreneurship decision. We thus propose a theoretical framework for the entrepreneurship decision in China with a focus on the role of institutional environment. Using a life-histories survey data of 2,854 respondents from twenty cities in China, we find strong support for the impacts of the institutional environment and its interactions with other determinants of entrepreneurship decision.

How Does Privatization Work in China?

Using a comprehensive panel data set of China’s state-owned enterprises, we investigate the impacts of privatization, of different time sequences and extent of non-state ownership, on social welfare and firm performance. Attention has been focused on the sources of gain in firm performance and the long-run impacts of privatization. It is found that the privatization of China’s state-owned enterprises was achieved with limited compromise on social welfare responsibilities, and significant gain in firm performance was obtained by motivating the management and reducing agency cost at the management level.