Online shopping is popular in promotion festivals like Singles' Day in China. With the presence of a flash crowd of customers during the limited time in E-commerce websites, the traditional system provides multi-dimensional product ranking lists including popularity based bought lists and viewed lists due to the customers' conformity to the popular products. However, the overloaded information may cause a long path to purchase for customers, who can not efficiently buy satisfied products within the time-limited festivals. To shorten customers' purchasing path, this paper designs an interests-based ranking method to automatically and efficiently aggregate an unified ranking list, by analyzing thousands of customers' shopping behavior to harness the dynamic and wide interests of crowd. Initially, we transform the local interaction behavior of flash crowd into the interests of crowd, which then is further personalized to an unified ranking for each customer. We combine user's temporal interests and current crowd wisdom to generate a new personalized unified ranking. The combined ranking is continuously adjusted with the recent fixed past time window data iteratively according to the evolution growth of crowd. An implementation test is carried out on a real world trace in large-scale interaction logs, which is from Alibaba Tmall with 12 million users and 29 thousand brands. It shows that combining the user's temporal interests and the aggregated crowd wisdom ranking improve the quality of item recommendations distinctly.

One of the challenges facing the networking industry today is to increase the profitability of Internet services. This calls for economic mechanisms that can enable providers to charge more for better services and collect a fair share of the increased revenues. In this paper, we present a generic pricing model for Internet services jointly offered by a group of providers. We show that noncooperative pricing strategies may lead to unfair distribution of profit and may even discourage future upgrades to the network. As an alternative, we propose a fair revenue-sharing policy based on the weighted proportional fairness criterion. We show that this fair allocation policy encourages collaboration among providers, and hence can produce higher profits for all providers. Based on the analysis, we suggest a scalable algorithm for providers to implement this policy in a distributed way and study its convergence property.

One of the critical challenges facing the networking industry today is to increase the profitability of Internet services. One well-known method in economics for increasing the revenues of a service is to segment its market through differentiation. However, special characteristics of Internet services, such as congestion externality, may complicate the design and provisioning of such offerings. In this paper, we study how a provider should price its services differentially based on their characteristics. By using a game-theoretic approach, we show that even with a simple two-class differentiated service model, if prices are not properly matched with service qualities, then the system may settle into an undesirable equilibrium similar to that in the classical "prisoner's dilemma" game. In addition, there may not even be a stable equilibrium under certain conditions. We then show that dynamic pricing approaches, in which prices are chosen according to users' relative preferences over different service classes, may be used to avoid such types of problems.

One of the challenges facing the networking industry today is to increase the profitability of Internet services. This calls for economic mechanisms that can enable providers to charge more for better services and collect a fair share of the increased revenues. In this papery we present a generic model for pricing Internet services that are jointly offered by a group of providers. We show that non-cooperative pricing strategies between providers may lead to unfair distribution of profit and may even discourage future upgrades to the network. As an alternative, we propose a fair revenue-sharing policy based on the weighted proportional fairness criterion. We show that this fair allocation policy encourages collaboration among providers and hence can produce higher profits for all providers. Based on the analysis, we suggest a scalable algorithm for providers to implement this policy in a distributed way and study its convergence property.

This paper explores the variations in mean queue length for stationary arrival processes with the same mean and autocorrelation functions, or equivalently, the same mean and power spectrum. Three types of processes, namely, two-state Markov-modulated Poisson processes, periodic-sequence modulated Poisson processes and processes generated by randomly filtering a white noise process, are investigated. Results show that the mean queue length can vary substantially for the first type of process, and can vary moderately for the second type of process, as the parameters of the processes are varied, subject to a specified mean and autocorrelation function. However, the mean queue lengths for the third type of arrival processes are determined by the input mean and autocorrelation functions. The results suggest that the queueing performance can be hard to predict from spectral data alone when the power in low frequencies is large.