Oisín Boydell

Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine

Search engines continue to struggle with the challenges presented by Web search: vague queries, impatient users and an enormous and rapidly expanding collection of unmoderated, heterogeneous documents all make for an extremely hostile search environment. In this paper we argue that conventional approaches to Web search -- those that adopt a traditional, document-centric, information retrieval perspective -- are limited by their refusal to consider the past search behaviour of users during future search sessions. In particular, we argue that in many circumstances the search behaviour of users is repetitive and regular; the same sort of queries tend to recur and the same type of results are often selected. We describe how this observation can lead to a novel approach to a more adaptive form of search, one that leverages past search behaviours as a means to re-rank future search results in a way that recognises the implicit preferences of communities of searchers. We describe and evaluate the I-SPY search engine, which implements this approach to collaborative, community-based search. We show that it offers potential improvements in search performance, especially in certain situations where communities of searchers share similar information needs and use similar queries to express these needs. We also show that I-SPY benefits from important advantages when it comes to user privacy. In short, we argue that I-SPY strikes a useful balance between search personalization and user privacy, by offering a unique form of anonymous personalization, and in doing so may very well provide privacy-conscious Web users with an acceptable approach to personalized search.

From social bookmarking to social summarization: an experiment in community-based summary generation

We describe a novel document summarization technique that uses informational cues, such as social bookmarks or search queries, as the basis for summary construction by leveraging the snippet-generation capabilities of standard search engines. A comprehensive evaluation demonstrates how the social summarization technique can generate summaries that are of significantly higher quality that those produced by a number of leading alternatives.

Enhancing Case-Based, Collaborative Web Search

This paper describes and evaluates a case-based approach to personalizing Web search by post-processing the results returned by a Web search engine to reflect the interests of a community of like-minded searchers. The search experiences of a community of users are captured as a case base of textual cases, which serves as a way to bias future search results in line with community interests.

Capturing community search expertise for personalized web search using snippet-indexes

We describe and evaluate an approach to capturing and re-using search expertise within a community of like minded searchers, such as the employees of a company or organisation. Within knowledge based industries, search expertise - the ability to quickly and accurately locate information according to a specific information need - is an important corporate asset and in our approach we attempt to capture this knowledge by mining the title and snippet texts of results that have been selected by community members in response to their queries. Our assumption is that the snippet text of a result must play a role in helping users to judge the initial relevance of that result and that the snippet terms of selected results must contain especially informative terms about the goals and preferences of the searchers. In other words, results are selected because the user recognises certain combinations of terms in their snippets which are related to their information needs. Our approach seeks to build a community-based snippet index that reflects the evolving interests of a group of searchers. This index is then used to re-rank the results returned by some underlying search engine by boosting the ranking of key results that have been frequently selected for similar queries by community members in the past.

Title and snippet based result re-ranking in collaborative web search

Collaborative Web search is a form of meta-search that manipulates the results of underlying Web search engines in response to the learned preferences of a given community of users. Results that have previously been selected in response to similar queries by community members are promoted in the returned results. However, promotion is limited to these previously-selected results and in this paper we describe and evaluate how relevant results without a selection history can also be promoted by exploiting snippet-text and title similarities.

Manipulating the relevance models of existing search engines

Collaborative search refers to how the search behavior of communities of users can be used to influence the ranking of search results. In this poster we describe how this technique, as instantiated in the I-SPY meta-search engine can be used as a general mechanism for implementing a different relevance feedback strategy. We evaluate a relevance feedback strategy based on anchor-text and query similarity using the TREC2004 Terabyte track document collection.

Deep learning at the shallow end: Malware classification for non-domain experts

Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.

TopicListener: Observing Key Topics from Multi-Channel Speech Audio Streams

Speech audio often encapsulates huge volumes of information which traditionally has been challenging to mine and analyse using automated methods. For example, call centres often handle many simultaneous telephone conversations between customers and call centre agents where, apart from relying on limited manual reporting by individual call centre agents, the content, themes and topics of the conversations are not analysed in any depth. In recent years there have been significant improvements in both the accuracy and cost of automated speech-to-text transcription technologies which can be applied in the call centre environment. We introduce TopicListener, which combines advanced topic modelling techniques with automatic speech transcription to identify key themes and topics across large volumes of recorded audio conversions as well as providing a novel means to explore and visualise the correlation and evolution of topics over time.

Community-based snippet-indexes for pseudo-anonymous personalization in web search

We describe and evaluate an approach to personalizing Web search that involves post-processing the results returned by some underlying search engine so that they re .ect the interests of a community of like-minded searchers.To do this we leverage the search experiences of the community by mining the title and snippet texts of results that have been selected by community members in response to their queries. Our approach seeks to build a community-based snippet index that re .ects the evolving interests of a group of searchers. This index is then sed to re-rank the results returned by the underlying search engine by boosting the ranking of key results that have been freq ently selected for similar q eries by community members in the past.

Evaluating the impact of selection noise in community-based web search

The I-SPY meta-search engine uses a technique called collaborative Web search to leverage the past search behaviour (queries and selections) of a community of users in order to promote search results that are relevant to the community. In this paper we describe recent studies to clarify the benefits of this approach in situations when the behaviour of users cannot be relied upon in terms of their ability to consistently select relevant results during search sessions.