Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis

Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer’s generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers’ generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising

Secure agent data integrity shield

In the rapidly expanding field of E-Commerce, mobile agent is the emerging technology that addresses the requirement of intelligent filtering/processing of information. This paper will address the area of mobile agent data integrity protection. We propose the use of Secure Agent Data Integrity Shield (SADIS) as a scheme that protects the integrity of data collected during agent roaming. With the use of a key seed negotiation protocol and integrity protection protocol, SADIS protects the secrecy as well as the integrity of agent data. Any illegal data modification, deletion, or insertion can be detected either by the subsequent host or the agent butler. Most important of all, the identity of each malicious host can be established. To evaluate the feasibility of our design, a prototype has been developed using Java. The result of benchmarking shows improvement both in terms of data and time efficiency

The effect of clustering on the precision of estimation : a thesis presented in partial fulfilment of the requirements for the degree of Master of Business Studies in Marketing at Massey University

The effect of clustering interval on design effect may be important in selection of alternative sampling designs by evaluating the cost-efficiency in the context of face-to-face interview surveys. There has been little work in investigating this effect in New Zealand. This study attempts to investigate this effect by using data from a two-stage sampling face-to-face interview survey. Seventeen stimulated samples are generated. A simple method, design effect =msb /ms, is developed to estimate design effects for 81 variables for both the simulated samples and the original sample. These estimated design effects are used to investigate the effect of clustering interval. This study also investigates the effect of cluster size. The results indicate that clustering interval has little influence on design effect but cluster size substantial influence. The evaluation of the cost-efficiency in alternative clustering intervals is discussed. As an improvement in the efficiency of a sample design by an increase in clustering interval can not be justified by the increase in cost, it seems that the sample design with the smallest clustering interval is the best. An alternative method design effect ≈ mr2 is also discussed and tested in estimating design effects. The result indicates that the applicability of design effect ≈ mr2 is the same as that of design effect = msb /ms