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Behaviour in virtual learning environments (VLE), including travel, gaze, manipulate, gesture and conversation, offer considerable information about the user's implicit interest. The purpose of this study is to find an approach for user interest mining via behaviour analysis in a VLE.

According to research in psychology, any interaction in a VLE has implications for the user's implicit interest. In order to mine a user's implicit interest, an explicit interaction‐interest model needs to be established. This paper presents findings from the concept classification of behaviour in a VLE. Based on this classification, the paper proposes a hierarchical interaction model. In this model the relation between interaction and user interest can be described and used to improve system performance.

In the experimental prototype the authors found that user‐implicit interest could be mined via stages of web mining, i.e. capture the user's original gesture signal, data pre‐process, pattern discovery, interaction goal and interest mining. The mined user's interest information can be used to update the state of local interest, leading to a reduction in network traffic and promotion of better system performance.

This is an original study using behaviour analysis for interest mining in e‐learning. Research on interest mining in e‐learning focused on content mining or search engine and usage mining in web courses. The paper provides valuable clues regarding user interest mining in a VLE, in which the context is different from usual web courses. The research output can be implemented widely, including online learning, and especially in the VLE.

This paper aims to explore the feasibility of using web‐mining technology on learning object (LO) usage information to discover the LO relation pattern and provide valuable recommendations on related learning resources. Design/methodology/approach – This paper proposes three kinds of learning object relation patterns and gives a specific definition of each pattern based on analysing the learners' usage data stored in the learning object repository. These relation patterns can be used to make effective recommendations to learners.

LO usage data indicate the potential relation patterns between LOs. By using web‐mining technology on the usage data, it is possible to discover valuable relation patterns.

The authors propose a set of LO relation patterns and indicate how they are closely related to users' learning behaviour.

The purpose of this guest editorial is to introduce the papers in this special issue.

A brief introduction about the issue of web‐mining applications in e‐commerce and e‐services is provided, along with a summary of the main contributions of the papers that are included in the special issue.

The value of web‐mining techniques can be enhanced through applying them to real environments such as e‐commerce and e‐services. The research fields of web mining, e‐commerce and e‐services can also be expanded.

An overview of the special issue and related research is provided in this paper.