Evelyn Balfe

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.

Further Experiments on Collaborative Ranking in Community-Based Web Search

As the search engine arms-race continues, search engines are constantly looking for ways to improve the manner in which they respond to user queries. Given the vagueness of Web search queries, recent research has focused on ways to introduce context into the search process as a means of clarifying vague, under-specified or ambiguous query terms. In this paper we describe a novel approach to using context in Web search that seeks to personalize the results of a generic search engine for the needs of a specialist community of users. In particular we describe two separate evaluations in detail that demonstrate how the collaborative search method has the potential to deliver significant search performance benefits to end-users while avoiding many of the privacy and security concerns that are commonly associated with related personalization research.

Anonymous personalization in collaborative web search

We present an innovative approach to Web search, called collaborative search, that seeks to cope with the type of vague queries that are commonplace in Web search. We do this by leveraging the search behaviour of previous searchers to personalize future result-lists according to the implied preferences of a community of like-minded individuals. This technique is implemented in the I-SPY meta-search engine and we present the results of a live-user trial which indicates that I-SPY can offer improved search performance when compared to a benchmark search engine, across a variety of performance metrics. In addition, I-SPY achieves its level of personalization while preserving the anonymity of individual users, and we argue that this offers unique privacy benefits compared to alternative approaches to personalization.

An analysis of query similarity in collaborative web search

Web search logs provide an invaluable source of information regarding the search behaviour of users. This information can be reused to aid future searches, especially when these logs contain the searching histories of specific communities of users. To date this information is rarely exploited as most Web search techniques continue to rely on the more traditional term-based IR approaches. In contrast, the I-SPY system attempts to reuse past search behaviours as a means to re-rank result-lists according to the implied preferences of like-minded communities of users. It relies on the ability to recognise previous search sessions that are related to the current target search by looking for similarities between past and current queries. We have previously shown how a simple model of query similarity can significantly improve search performance by implementing this reuse approach. In this paper we build on previous work by evaluating alternative query similarity models.

Case-Based Collaborative Web Search

Web search is typically memory-less, in the sense that each new search query is considered afresh and ‘solved’ from scratch. We believe that this reflects the strong information retrieval bias that has influenced the development of Web search engines. In this paper we argue for the value of a fresh approach to Web search, one that is founded on the notion of reuse and that seeks to exploit past search histories to answer future search queries. We describe a novel case-based technique and evaluate it using live-user data. We show that it can deliver significant performance benefits when compared to alternative strategies including meta-search.

I-SPY — Anonymous, Community-Based Personalization by Collaborative Meta-Search

Today’s Web search engines often fail to satisfy the needs of their users, in part because search engines do not respond well to the vague queries of many users. One potentially promising solution involves the introduction of context into the search process as a means of elaborating vague queries. In this paper we describe and evaluate a novel approach to using context in Web search that adapts a generic search engine for the needs of a specialist community of users. This collaborative search method enjoys significant performance benefits and avoids the privacy and security concerns that are commonly associated with related personalization research.

A comparative analysis of query similarity metrics for community-based web search

Collaborative Web search is a community-based approach to adaptive Web search that is fundamentally case-based: the results of similar past search sessions are reused in response to new target queries. Previously, we have demonstrated that this approach to Web search can offer communities of like-minded searchers significant benefits when it comes to result relevance. In this paper we examine the fundamental issue of query similarity that drives the selection and reuse of previous search sessions. In the past we have proposed the use of a relatively simple form of query similarity, based on the overlap of query-terms. In this paper we examine and compare a collection of 10 alternative metrics that use different types of knowledge (query-terms vs. result-lists vs. selection behaviour) as the basis for similarity assessment.

Query Mining for Community Based Web Search

We present an innovative approach to personalized Web search that exploits the search behaviour of a community of users to re-rank future result-lists according to the implied preferences of this group. Evaluation results demonstrate the precision and recall benefits of our collaborative search technique and we show how personalization can be achieved without the need for individual user profiling.