Identifying legitimate Web users and bots with different traffic profiles — an Information Bottleneck approach

Artificial intelligence in E-Commerce: a bibliometric study and literature review

This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.

HTTP-level e-commerce data based on server access logs for an online store

Web server logs have been extensively used as a source of data on the characteristics of Web traffic and users' navigational patterns. In particular, Web bot detection and online purchase prediction using methods from artificial intelligence (AI) are currently key areas of research. However, in reality, it is hard to obtain logs from actual online stores and there is no common dataset that can be used across different studies. Moreover, there is a lack of studies exploring Web traffic over a longer period of time, due to the unavailability of long-term data from server logs. The need to develop reliable models of Web traffic, Web user navigation, and e-customer behaviour calls for an up-to-date, large-volume e-commerce dataset on Web traffic. Similarly, AI problems require a sufficient amount of solid, real-life data to train and validate new models and methods. Thus, to meet a demand of a publicly available long-term e-commerce dataset, we collected access log data describing the operation of an online store over a six-month period. Using a program written in the C# language, data were aggregated, transformed, and anonymized. As a result, we release this EClog dataset in CSV format, which covers 183 days of HTTP-level e-commerce traffic. The data will be beneficial for research in many areas, including computer science, data science, management, and sociology.