Maurice Coyle

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.

Google Shared. A Case-Study in Social Search

Web search is the dominant form of information access and everyday millions of searches are handled by mainstream search engines, but users still struggle to find what they are looking for, and there is much room for improvement. In this paper we describe a novel and practical approach to Web search that combines ideas from personalization and social networking to provide a more collaborative search experience. We described how this has been delivered by complementing, rather than competing with, mainstream search engines, which offers considerable business potential in a Google-dominated search marketplace.

Supporting intelligent Web search

Search engines continue to struggle to provide everyday users with a service capable of delivering focussed results that are relevant to their information needs. Moreover, traditional search engines really only provide users with a starting point for their information search. That is, upon selecting a page from a search result list, the interaction between user and search engine is effectively over and the user must continue their search alone. In this article, we argue that a comprehensive search service needs to provide the user with more help, both at the result list level and beyond, and we outline some recommendations for intelligent Web search support. We introduce the SearchGuide Web search support system and we describe how it fulfils the requirements for a search support system, providing evaluation results where applicable.

Towards a reputation-based model of social web search

While web search tasks are often inherently collaborative in nature, many search engines do not explicitly support collaboration during search. In this paper, we describe HeyStaks (www.heystaks.com), a system that provides a novel approach to collaborative web search. Designed to work with mainstream search engines such as Google, HeyStaks supports searchers by harnessing the experiences of others as the basis for result recommendations. Moreover, a key contribution of our work is to propose a reputation system for HeyStaks to model the value of individual searchers from a result recommendation perspective. In particular, we propose an algorithm to calculate reputation directly from user search activity and we provide encouraging results for our approach based on a preliminary analysis of user activity and reputation scores across a sample of HeyStaks users.

A Case Study of Collaboration and Reputation in Social Web Search

Although collaborative searching is not supported by mainstream search engines, recent research has highlighted the inherently collaborative nature of many Web search tasks. In this article, we describe HeyStaks, a collaborative Web search framework that is designed to complement mainstream search engines. At search time, HeyStaks learns from the search activities of other users and leverages this information to generate recommendations based on results that others have found relevant for similar searches. The key contribution of this article is to extend the HeyStaks social search model by considering the search expertise, or reputation, of HeyStaks users and using this information to enhance the result recommendation process. In particular, we propose a reputation model for HeyStaks users that utilise the implicit collaboration events that take place between users as recommendations are made and selected. We describe a live-user trial of HeyStaks that demonstrates the relevance of its core recommendations and the ability of the reputation model to further improve recommendation quality. Our findings indicate that incorporating reputation into the recommendation process further improves the relevance of HeyStaks recommendations by up to 40%.

A Case-Based Perspective on Social Web Search

Web search is the main way for millions of users to access information every day, but we continue to struggle when it comes to finding the right information at the right time. In this paper we build on recent work to describe and evaluate a new application of case-based Web search, one that focuses on how experience reuse can support collaboration among searchers. Special emphasis is placed on the development of a case-based system that is compatible with existing search engines. We also describe the results of a live-user deployment.

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.

ASSIST: adaptive social support for information space traversal

Finding relevant information in a hyperspace has been a much studied problem for many years. With the emergence of so called Web 2.0 technologies we have seen the use of social systems for retrieval tasks increasing dramatically. Each system collects and exploits its own pool of community wisdom for the benefit of its users. In this paper we suggest a form of retrieval which exploits the pools of wisdom of multiple social technologies, specifically social search and social navigation. The paper details the added user benefits of merging several sources of social wisdom. We present details of the ASSIST engine developed to integrate social support mechanisms for the users of information repositories. The goal of this paper is to present the main features of the integrated community-based personalization engine that we have developed in order to improve retrieval in the hyperspace of information resources. It also reports the results of an empirical study of this technology.

(Web Search)shared: Social Aspects of a Collaborative, Community-Based Search Network

Collaborative Web search (CWS) is a community-based approach to Web search that supports the sharing of past result selections among a group of related searchers so as to personalize result-lists to reflect the preferences of the community as a whole. In this paper, we present the results of a recent live-user trial which demonstrates how CWS elicits high levels of participation and how the search activities of a community of related users form a type of social search network.