Intelligent user search behaviour knowledge discovery

It is important for Web search engine providers to study user behaviour in order to have a better understanding of how customers interact with search engines so that they can improve users' overall search experience. However, user behaviour in a search engine is complicated and affected by various factors, e.g. query length, intention/context/time when queries are submitted, etc. It is interesting to find answers to questions such as “whether loyal users are more likely to issue short length queries or medium length queries? If so, is that behaviour linked with high click through rate or is it linked with the user's previous search experience?” In this paper we argue that user behaviour should be better analysed from a subjective angle and introduce a granular analysis algorithm to intelligently extract user behaviour knowledge in a human-centric way to answer above questions. We study six variables relating to user behaviour study and demonstrate how fuzzy association rules mining based on mass assignment theory can intelligently analyse user activity patterns in a large scale Web search log data set.