Séamus Lawless

The Workshop on Evaluation of Personalisation in Information Retrieval (WEPIR 2018) was held in conjunction with the ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2018) in New Brunswick, USA. The purpose of WEPIR 2018 was to bring together researchers from different backgrounds, interested in advancing the evaluation of personalisation in information retrieval. The workshop focused on developing a common understanding of the challenges, requirements and practical limitations of evaluation of personalisation in information retrieval.

Brendan Spillane, Séamus Lawless & Vincent Wade

Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries. Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even more effective query expansion methods. Co-occurrence statistics, tag-tag relationships, and semantic matching approaches are among those favored by previous research. However, user profiles which only contain a user's past annotation information may not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system. We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile, respectively. The results of an in-depth experimental evaluation, performed on two real-world datasets using different external corpora, show that our approach outperforms traditional techniques, including existing non-personalized and personalized query expansion methods.

The ability to understand the expertise of users in Social Networking Sites (SNSs) is a key component for delivering effective information services such as talent seeking and user recommendation. However, users are often unwilling to make the effort to explicitly provide this information, so existing methods aimed at user expertise discovery in SNSs primarily rely on implicit inference. This work aims to infer a user's expertise based on their posts on the popular micro-blogging site Twitter. The work proposes a sentiment-weighted and topic relation-regularized learning model to address this problem. It first uses the sentiment intensity of a tweet to evaluate its importance in inferring a user's expertise. The intuition is that if a person can forcefully and subjectively express their opinion on a topic, it is more likely that the person has strong knowledge of that topic. Secondly, the relatedness between expertise topics is exploited to model the inference problem. The experiments reported in this paper were conducted on a large-scale dataset with over 10,000 Twitter users and 149 expertise topics. The results demonstrate the success of our proposed approach in user expertise inference and show that the proposed approach outperforms several alternative methods.

Purpose – With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion. Design/methodology/approach – The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as various latent semantic models trained on monolingual documents and cross-lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods. Findings – Experimental results suggest that latent semantic-weighted user profile representation techniques are superior to frequency-based methods, and are particularly suita...

The workshop aims to bring together researchers and practitioners to review and discuss ways of providing effective access to large-scale collections of cultural heritage content. The scale, variety and availability of cultural heritage content, combined with the variety of user groups with respect to background knowledge, specialist experience and needs is challenging in the context of existing access methods. In particular, we consider going beyond keyword search in large-scale cultural heritage digital libraries, in support of exploration and discovery. Our purpose for the workshop is to consider the opportunities and challenges presented by new and existing technologies, as well as the needs and experiences of diverse user communities. Our goal is to assess the current state-of the-art, to identify opportunities and establish future research priorities, informed by the combined knowledge and experience of academics and practitioners