Search results for "Web store"
showing 4 items of 4 documents
Application of the Information Bottleneck method to discover user profiles in a Web store
2018
The paper deals with the problem of discovering groups of Web users with similar behavioral patterns on an e-commerce site. We introduce a novel approach to the unsupervised classification of user sessions, based on session attributes related to the user click-stream behavior, to gain insight into characteristics of various user profiles. The approach uses the agglomerative Information Bottleneck (IB) algorithm. Based on log data for a real online store, efficiency of the approach in terms of its ability to differentiate between buying and non-buying sessions was validated, indicating some possible practical applications of the our method. Experiments performed for a number of session sampl…
Ideal types of online shoppers : a qualitative analysis of online shopping behavior
2018
Due to the growing popularity of online shopping, there is a growing demand for understanding the motives and behaviour of online shoppers. This study aims to increase this understanding by examining online shopping behaviour from the perspective of UTAUT2 theory integrated with self-efficacy and risk avoidance components. The thematically analysed data from 31 participants highlights the unique aspects of online shoppers by grouping them into ideal types, presenting the data as extensively as possible. An ideal type is an analytical construct used to ascertain similarities and deviations to concrete cases in an individual phenomenon. This study discovered five ideal types of online shopper…
Verification of Web traffic burstiness and self-similarity for multiple online stores
2017
Developing realistic Web traffic models is essential for a reliable Web server performance evaluation. Very significant Web traffic properties that have been identified so far include burstiness and self-similarity. Very few relevant studies have been devoted to e-commerce traffic, however. In this paper, we investigate burstiness and self-similarity factors for seven different online stores using their access log data. Our findings show that both features are present in all the analyzed e-commerce datasets. Furthermore, a strong correlation of the Hurst parameter with the average request arrival rate was discovered (0.94). Estimates of the Hurst parameter for the Web traffic in the online …
Application of neural network to predict purchases in online store
2016
A key ability of competitive online stores is effective prediction of customers’ purchase intentions as it makes it possible to apply personalized service strategy to convert visitors into buyers and increase sales conversion rates. Data mining and artificial intelligence techniques have proven to be successful in classification and prediction tasks in complex real-time systems, like e-commerce sites. In this paper we proposed a back-propagation neural network model aiming at predicting purchases in active user sessions in a Web store. The neural network training and evaluation was performed using a set of user sessions reconstructed from server log data. The proposed neural network was abl…