Analysis of Transaction Logs from National Museums Liverpool

The websites of Cultural Heritage institutions attract the full range of users, from professionals to novices, for a variety of tasks. However, many institutions are reporting high bounce rates and therefore seeking ways to better engage users. The analysis of transaction logs can provide insights into users’ searching and navigational behaviours and support engagement strategies. In this paper we present the results from a transaction log analysis of web server logs representing user-system interactions from the seven websites of National Museums Liverpool (NML). In addition, we undertake an exploratory cluster analysis of users to identify potential user groups that emerge from the data. We compare this with previous studies of NML website users

A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization

Manifold regularization is a commonly used technique in semi-supervised learning. It enforces the classification rule to be smooth with respect to the data-manifold. Here, we derive sample complexity bounds based on pseudo-dimension for models that add a convex data dependent regularization term to a supervised learning process, as is in particular done in Manifold regularization. We then compare the bound for those semi-supervised methods to purely supervised methods, and discuss a setting in which the semi-supervised method can only have a constant improvement, ignoring logarithmic terms. By viewing Manifold regularization as a kernel method we then derive Rademacher bounds which allow for a distribution dependent analysis. Finally we illustrate that these bounds may be useful for choosing an appropriate manifold regularization parameter in situations with very sparsely labeled data.Virtual/online event due to COVID-19Interactive IntelligencePattern Recognition and Bioinformatic