Information retrieval has become very popular over the last decade with the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as intranets and digital libraries. Such collections are the focus of the recently started AutoAdapt project1. The project seeks to aid user search by providing well-structured domain knowledge to assist query modification and navigation. There are two challenges: acquiring the domain knowledge and adapting it automatically to the specific interest of the user community. At the workshop we will demonstrate an implemented prototype that serves as a starting point on the way to truly adaptive search

De Roeck, A

Dignum, S

Fasli, M

Kim, Y

Kruschwitz, U

Song, D

English

Information retrieval has become very popular over the last

decade with the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as intranets and digital libraries. Such collections are the focus of the AutoAdapt project. The project seeks to aid user search by providing well structured domain knowledge to assist query modification and navigation.



There are two challenges: acquiring the domain knowledge and adapting it automatically to the specific interest of the user community. The paper introduces an implemented prototype that serves as a starting point on the

way to truly adaptive search

Dignum, Stephen

Kim, Yunhyong

Kruschwitz, Udo

Song, Dawei

Fasli, Maria

De Roeck, Anne

Open Research Online

Open Research OnlineThe Open University’s repository of research publicationsand other research outputsMoving towards adaptive searchConference or Workshop ItemHow to cite:Dignum, Stephen; Kim, Yunhyong; Kruschwitz, Udo; Song, Dawei; Fasli, Maria and De Roeck, Anne (2009).Moving towards adaptive search. In: Workshop on Advanced Technologies for Digital Libraries, 8 Sep 2009, Trento,Italy.For guidance on citations see FAQs.c© 2009 AT4DLVersion: Not SetLink(s) to article on publisher’s website:http://www.cacaoproject.eu/fileadmin/media/AT4DL/paper-08.pdfCopyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyrightowners. For more information on Open Research Online’s data policy on reuse of materials please consult the policiespage.oro.open.ac.ukMoving towards Adaptive SearchStephen Dignum1, Yunhyong Kim2, Udo Kruschwitz1, Dawei Song2, MariaFasli1, and Anne De Roeck31University of Essex, Colchester, UK{sandig, udo, mfasli}@essex.ac.uk2Robert Gordon University, Aberdeen, UK{ykim1, d.song}@rgu.ac.uk3Open University, Milton Keynes, UKa.deroeck@rgu.ac.ukAbstract. Information retrieval has become very popular over the lastdecade with the advent of the Web. Nevertheless, searching on the Webis very different to searching on smaller, often more structured collectionssuch as intranets and digital libraries. Such collections are the focus ofthe recently started AutoAdapt project1. The project seeks to aid usersearch by providing well-structured domain knowledge to assist query mod-ification and navigation. There are two challenges: acquiring the domainknowledge and adapting it automatically to the specific interest of the usercommunity. At the workshop we will demonstrate an implemented proto-type that serves as a starting point on the way to truly adaptive search.1 IntroductionDocument retrieval systems have been around for more than fifty years, and earlysystems exploited similar structures that we have in modern digital libraries, suchas author name, book title, and keywords [9]. More recently we have witnessed amajor shift towards search on the “Web”. However, search techniques that workwell on the Web do not necessarily work equally well on collections that haveother characteristics, e.g. domain-specific or more structured collections, as foundin intranets, digital libraries and on local Web sites. Retrieval from intranets, forexample, behaves unlike Web search [14]. For instance, standard ranking functions(e.g., PageRank [3] and HITS [7]) that work well for Web collections are less effec-tive on intranets. Furthermore, the terminology, structure, and services providedwithin such collections are selected to meet organisational requirements, and, con-sequently, a considerable amount of time is spent by users trying to learn the do-main characteristics even before they are able to identify the adequate questionsto be submitted to a search system. From an information systems perspective,thesauri and classification schemes should be developed and adapted to matchinformation contained in such collections [1].The approach that the AutoAdapt project takes is to maintain (or adapt) suchstructures automatically. We are looking at search as well as navigation withindomain-specific document collections and our aim is to satisfy a user’s informationrequest effectively by learning from the entire user population and incorporating1 http://autoadaptproject.org2 Stephen Dignum et althis learned knowledge in a constantly adapting domain model which assists auser in the search process. To support such adaptation we investigate how do-main models are explored using clickthrough data that link up query modificationsteps and associate clicked documents with queries. This provides a context-richenvironment where learning algorithms can identify new terms and relationshipsto add to a model, remove outdated or irrelevant terms and relationships, andmodify weights as certain paths become more popular.The workshop will be an opportunity to present a working prototype that al-lows system-guided search of document collections using automatically constructeddomain knowledge as well as existing knowledge structures as used in digital li-braries. It will also be a useful forum to discuss our current research focus, theautomatic adaptation of the system using the logged user interactions.2 Related WorkThere is a wealth of related work in log analysis, interactive search and otherareas, e.g., [5, 12]. Due to limited space we will only present a few findings thatshould serve as motivations for our own work. First of all, we know that users arereluctant to leave any explicit feedback when they search a document collection[10]. However, implicit feedback, e.g., the analysis of log records, has been shown tobe good at approximating explicit feedback. For example, users often reformulatetheir query and such patterns can help in learning an improved ranking function[6]. The same methods have shown to improve an adaptive domain model on alocal Web site [8].We can ask, however, do users want assisted search in the first place? Firstof all, digital libraries are characterized by much more structured knowledge thanWeb sites. This makes system-guided search a natural option as evidenced bythe success of Aquabrowser2 as a tool to access digital libraries. More generallythough, there is also evidence that users want support in proposing keywords butthey ultimately want to stay in control about what is being submitted as a query[16]. Furthermore, despite the risk of offering irrelevant suggestions in a system-guided search system, users might prefer having them rather than not [15]. On theother hand, it has also been shown that users are more inclined to submit newqueries or resubmit modified queries than to navigate from the result set in a searchenvironment that supports search and navigation [11]. Perhaps the best evidencefor an interactive search system is the fact that all big Web search engines haverecently added more and more interactive features, e.g., Google’s Wonderwheel3.We are in line with what Belkin calls the challenge of all challenges in IR atthe moment, to move beyond the limited, inherently non-interactive models of IRto truly interactive systems [2]. Our aim is to go beyond static interaction patternsand move to adaptive retrieval exploiting the implicit feedback that users leavewhen searching and navigating a document collection. Building adaptive domainmodels for digital libraries and other collections is our approach to capturing andutilizing collective intelligence [13].2 http://www.aquabrowser.com/3 http://www.googlewonderwheel.comMoving towards Adaptive Search 3Fig. 1. Screenshot of AutoAdapt Demo System.3 AutoAdapt PrototypeIn Figure 1, we can see a screenshot of our demonstration system running on anintranet. In this particular case the domain model is automatically extracted fromthe document collection. The user submits a search query, this results in a numberof matches (documents, book titles, etc.) being returned. Using the query terms,a segment of the domain model is displayed. The user can traverse the domainmodel by clicking on displayed terms. On term selection the list of suggestedterms is updated. The user can then add the term to the existing query or use asa new query. The graph representations of domain models has been discussed inthe literature, e.g. [4]. They are not the focus of our research but a useful tool.The logging structure records a number of user decisions without the need forexplicit feedback. What we are logging is not simply the action a user has taken(e.g., selecting a query modification, clicking a term in the domain model, selectinga match) but also recording what options a user has not taken but which have beenavailable. This provides relative judgements that can be used to train classifiers[6, 8].We have started to apply a number of techniques to turn the logged interactionsinto adaptive models and would like to discuss this at the workshop.AcknowledgementsAutoAdapt is funded by EPSRC grants EP/F035357/1 and EP/F035705/1. TheJIT visualisation toolkit4 was used for the domain model visualisation.4 http://blog.thejit.org/javascript-information-visualization-toolkit-jit4 Stephen Dignum et alReferences1. M. J. Bates. The cascade of interactions in the digital library interface. InformationProcessing and Management, 38(3):381–400, 2002.2. N. J. Belkin. Some(what) grand challenges for information retrieval. SIGIR Forum,42(1):47–54, 2008.3. S. Brin and L. Page. The Anatomy of a Large-Scale Hypertextual Web Search Engine.In Proceedings of the Seventh International World Wide Web Conference (WWW7),pages 107–117, Brisbane, 1998.4. J. W. Buzydlowski, H. D. White, and X. Lin. Term co-occurrence analysis as aninterface for digital libraries. In Joint Conference on Digital Libraries, pages 133–144, 2001.5. J. Jansen, A. Spink, and I. Taksa, editors. Handbook of Research on Web Log Anal-ysis. IGI, 2008.6. T. Joachims and F. Radlinski. Search engines that learn from implicit feedback.IEEE Computer, 40(8):34–40, 2007.7. J. M. Kleinberg. Authoritative Sources in a Hyperlinked Environment. In Proceedingsof the 9 th ACM-SIAM Symposium on Discrete Algorithms, pages 668–677. ACM,1998.8. D. Lungley and U. Kruschwitz. Automatically maintained domain knowledge: Initialfindings. In Proceedings of ECIR, pages 739–743, 2009.9. C. Manning, R. Prabhakar, and H. Schu¨tze. Introduction to Information Retrieval.Cambridge University Press, Cambridge, 2008.10. K. Markey. Twenty-five years of end-user searching, Part 1: Research findings.Journal of the American Society for Information Science and Technology (JASIST),58(8):1071–1081, June 2007.11. M. Mat-Hassan and M. Levene. Associating Search and Navigation BehaviorThrough Log Analysis. JASIST, 56(9):913–934, 2005.12. F. Silvestri. Mining Query Logs: Turning Search Usage Data into Knowledge. Foun-dations and Trends in Information Retrieval. Now Publisher, 2009. Forthcoming.13. J. Surowiecki. The Wisdom of Crowds. Anchor, 2005.14. M. White. Making Search Work: Implementing Web, Intranet and Enterprise Search.Facet Publishing, 2007.15. R. W. White, M. Bilenko, and S. Cucerzan. Studying the Use of Popular Destinationsto Enhance Web Search Interaction. In Proceedings of SIGIR’07, pages 159–166,Amsterdam, 2007.16. R. W. White and I. Ruthven. A Study of Interface Support Mechanisms for Inter-active Information Retrieval. JASIST, 57(7):933–948, 2006.

Moving towards adaptive search

University of Essex Research Repository

Moving towards Adaptive SearchStephen Dignum1, Yunhyong Kim2, Udo Kruschwitz1, Dawei Song2, MariaFasli1, and Anne De Roeck31University of Essex, Colchester, UK{sandig, udo, mfasli}@essex.ac.uk2Robert Gordon University, Aberdeen, UK{ykim1, d.song}@rgu.ac.uk3Open University, Milton Keynes, UKa.deroeck@rgu.ac.ukAbstract. Information retrieval has become very popular over the lastdecade with the advent of the Web. Nevertheless, searching on the Webis very different to searching on smaller, often more structured collectionssuch as intranets and digital libraries. Such collections are the focus of theAutoAdapt project1. The project seeks to aid user search by providing well-structured domain knowledge to assist query modification and navigation.There are two challenges: acquiring the domain knowledge and adaptingit automatically to the specific interest of the user community. The paperintroduces an implemented prototype that serves as a starting point on theway to truly adaptive search.1 IntroductionDocument retrieval systems have been around for more than fifty years, and earlysystems exploited similar structures that we have in modern digital libraries, suchas author name, book title, and keywords [9]. More recently we have witnessed amajor shift towards search on the “Web”. However, search techniques that workwell on the Web do not necessarily work equally well on collections that haveother characteristics, e.g. domain-specific or more structured collections, as foundin intranets, digital libraries and on local Web sites. Retrieval from intranets, forexample, behaves unlike Web search [14]. For instance, standard ranking functions(e.g., PageRank [3] and HITS [7]) that work well for Web collections are less effec-tive on intranets. Furthermore, the terminology, structure, and services providedwithin such collections are selected to meet organisational requirements, and, con-sequently, a considerable amount of time is spent by users trying to learn the do-main characteristics even before they are able to identify the adequate questionsto be submitted to a search system. From an information systems perspective,thesauri and classification schemes should be developed and adapted to matchinformation contained in such collections [1].The approach that the AutoAdapt project takes is to maintain (or adapt) suchstructures automatically. We are looking at search as well as navigation withindomain-specific document collections and our aim is to satisfy a user’s informationrequest effectively by learning from the entire user population and incorporating1 http://autoadaptproject.orgthis learned knowledge in a constantly adapting domain model which assists auser in the search process. To support such adaptation we investigate how do-main models are explored using clickthrough data that link up query modificationsteps and associate clicked documents with queries. This provides a context-richenvironment where learning algorithms can identify new terms and relationshipsto add to a model, remove outdated or irrelevant terms and relationships, andmodify weights as certain paths become more popular.Here we present a working prototype that allows system-guided search of doc-ument collections using automatically constructed domain knowledge as well asexisting knowledge structures as used in digital libraries.2 Related WorkThere is a wealth of related work in log analysis, interactive search and otherareas, e.g., [5, 12]. Due to limited space we will only present a few findings thatshould serve as motivations for our own work. First of all, we know that users arereluctant to leave any explicit feedback when they search a document collection[10]. However, implicit feedback, e.g., the analysis of log records, has been shown tobe good at approximating explicit feedback. For example, users often reformulatetheir query and such patterns can help in learning an improved ranking function[6]. The same methods have shown to improve an adaptive domain model on alocal Web site [8].We can ask, however, do users want assisted search in the first place? Firstof all, digital libraries are characterized by much more structured knowledge thanWeb sites. This makes system-guided search a natural option as evidenced bythe success of Aquabrowser2 as a tool to access digital libraries. More generallythough, there is also evidence that users want support in proposing keywords butthey ultimately want to stay in control about what is being submitted as a query[16]. Furthermore, despite the risk of offering irrelevant suggestions in a system-guided search system, users might prefer having them rather than not [15]. On theother hand, it has also been shown that users are more inclined to submit newqueries or resubmit modified queries than to navigate from the result set in a searchenvironment that supports search and navigation [11]. Perhaps the best evidencefor an interactive search system is the fact that all big Web search engines haverecently added more and more interactive features, e.g., Google’s Wonderwheel3.We are in line with what Belkin calls the challenge of all challenges in IR atthe moment, to move beyond the limited, inherently non-interactive models of IRto truly interactive systems [2]. Our aim is to go beyond static interaction patternsand move to adaptive retrieval exploiting the implicit feedback that users leavewhen searching and navigating a document collection. Building adaptive domainmodels for digital libraries and other collections is our approach to capturing andutilizing collective intelligence [13].2 http://www.aquabrowser.com/3 http://www.googlewonderwheel.comFig. 1. Screenshot of AutoAdapt Demo System.3 AutoAdapt PrototypeIn Figure 1, we can see a screenshot of our demonstration system running on anintranet. In this particular case the domain model is automatically extracted fromthe document collection. The user submits a search query, this results in a numberof matches (documents, book titles, etc.) being returned. Using the query terms,a segment of the domain model is displayed. The user can traverse the domainmodel by clicking on displayed terms. On term selection the list of suggestedterms is updated. The user can then add the term to the existing query or use asa new query. The graph representations of domain models has been discussed inthe literature, e.g. [4]. They are not the focus of our research but a useful tool.The logging structure records a number of user decisions without the need forexplicit feedback. What we are logging is not simply the action a user has taken(e.g., selecting a query modification, clicking a term in the domain model, selectinga match) but also recording what options a user has not taken but which have beenavailable. This provides relative judgements that can be used to train classifiers[6, 8].We have started to apply a number of techniques to turn the logged interactionsinto adaptive models which will be the focus of the next stage in the AutoAdaptproject.AcknowledgementsAutoAdapt is funded by EPSRC grants EP/F035357/1 and EP/F035705/1. TheJIT visualisation toolkit4 was used for the domain model visualisation.4 http://blog.thejit.org/javascript-information-visualization-toolkit-jitReferences1. M. J. Bates. The cascade of interactions in the digital library interface. InformationProcessing and Management, 38(3):381–400, 2002.2. N. J. Belkin. Some(what) grand challenges for information retrieval. SIGIR Forum,42(1):47–54, 2008.3. S. Brin and L. Page. The Anatomy of a Large-Scale Hypertextual Web Search Engine.In Proceedings of the Seventh International World Wide Web Conference (WWW7),pages 107–117, Brisbane, 1998.4. J. W. Buzydlowski, H. D. White, and X. Lin. Term co-occurrence analysis as aninterface for digital libraries. In Joint Conference on Digital Libraries, pages 133–144, 2001.5. J. Jansen, A. Spink, and I. Taksa, editors. Handbook of Research on Web Log Anal-ysis. IGI, 2008.6. T. Joachims and F. Radlinski. Search engines that learn from implicit feedback.IEEE Computer, 40(8):34–40, 2007.7. J. M. Kleinberg. Authoritative Sources in a Hyperlinked Environment. In Proceedingsof the 9 th ACM-SIAM Symposium on Discrete Algorithms, pages 668–677. ACM,1998.8. D. Lungley and U. Kruschwitz. Automatically maintained domain knowledge: Initialfindings. In Proceedings of ECIR, pages 739–743, 2009.9. C. Manning, R. Prabhakar, and H. Schu¨tze. Introduction to Information Retrieval.Cambridge University Press, Cambridge, 2008.10. K. Markey. Twenty-five years of end-user searching, Part 1: Research findings.Journal of the American Society for Information Science and Technology (JASIST),58(8):1071–1081, June 2007.11. M. Mat-Hassan and M. Levene. Associating Search and Navigation BehaviorThrough Log Analysis. JASIST, 56(9):913–934, 2005.12. F. Silvestri. Mining Query Logs: Turning Search Usage Data into Knowledge. Foun-dations and Trends in Information Retrieval. Now Publisher, 2009. Forthcoming.13. J. Surowiecki. The Wisdom of Crowds. Anchor, 2005.14. M. White. Making Search Work: Implementing Web, Intranet and Enterprise Search.Facet Publishing, 2007.15. R. W. White, M. Bilenko, and S. Cucerzan. Studying the Use of Popular Destinationsto Enhance Web Search Interaction. In Proceedings of SIGIR’07, pages 159–166,Amsterdam, 2007.16. R. W. White and I. Ruthven. A Study of Interface Support Mechanisms for Inter-active Information Retrieval. JASIST, 57(7):933–948, 2006.

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