Learn More
Personalisation is an important area in the field of IR that attempts to adapt ranking algorithms so that the results returned are tuned towards the searcher's interests. In this work we use query logs to build personalised ranking models in which user profiles are constructed based on the representation of clicked documents over a topic space. Instead of(More)
We investigate the utility of topic models for the task of personalizing search results based on information present in a large query log. We define generative models that take both the user and the clicked document into account when estimating the probability of query terms. These models can then be used to rank documents by their likelihood given a(More)
In this paper we present a longitudinal, naturalistic study of email behavior (n=47) and describe our efforts at isolating re-finding behavior in the logs through various qualitative and quantitative analyses. The presented work underlines the methodological challenges faced with this kind of research, but demonstrates that it is possible to isolate(More)
Collaborative filtering systems based on ratings make it easier for users to find content of interest on the Web and as such they constitute an area of much research. In this paper we first present a Bayesian latent variable model for rating prediction that models ratings over each user's latent interests and also each item's latent topics. We describe a(More)
Social tagging systems have recently become very popular as a method of categorising information online and have been used to annotate a wide range of different resources. In such systems users are free to choose whatever keywords or "tags" they wish to annotate each resource, resulting in a highly personalised, unrestricted vocabulary. While this freedom(More)
Micro-blogging services such as Twitter represent constantly evolving, user-generated sources of information. Previous studies show that users search over such content regularly, but are often dissatisfied with current search facilities. We argue that an enhanced understanding of the motivations for search would aid the design of improved search systems,(More)
Social tagging systems provide methods for users to cate-gorise resources using their own choice of keywords (or " tags ") without being bound to a restrictive set of predefined terms. Such systems typically provide simple tag recommendations to increase the number of tags assigned to resources. In this paper we extend the latent Dirichlet allocation topic(More)
Poor nutrition is fast becoming one of the major causes of ill-health and death in the western world. It is caused by a variety of factors including lack of nutritional understanding leading to poor choices being made when selecting which dishes to cook and eat. We wish to build systems which can recommend nutritious meal plans to users, however a crucial(More)