Vera Hollink

Monolingual Document Retrieval for European Languages

Recent years have witnessed considerable advances in information retrieval for European languages other than English. We give an overview of commonly used techniques and we analyze them with respect to their impact on retrieval effectiveness. The techniques considered range from linguistically motivated techniques, such as morphological normalization and compound splitting, to knowledge-free approaches, such as n-gram indexing. Evaluations are carried out against data from the CLEF campaign, covering eight European languages. Our results show that for many of these languages a modicum of linguistic techniques may lead to improvements in retrieval effectiveness, as can the use of language independent techniques.

Navigation behavior models for link structure optimization

Analysis of existing methods for automatic optimization of link structures shows that these methods rely heavily on assumptions about the preferences and navigation behavior of users. Authors often do not state these assumptions explicitly and do not evaluate whether the assumptions are consistent with the actual behavior of the users of the site. This is a serious deficiency as experiments with simulated users show that incorrect assumptions can easily lead to inefficient link structures. In this work we present a framework that gives a systematic overview of alternative assumptions. On the basis of the framework we can select a set of assumptions that best matches the navigation behavior of the users in the site’s log files. We also present a method for optimizing hierarchical navigation menus on the basis of the selected assumptions. This method can be used interactively under full control of a web master. The system proposes modifications of the structure and explains why these modifications lead to more efficient menus. Evaluation by means of a case study shows that the modifications that are proposed effectively reduce the expected navigation time while preserving the coherence of the menu structure.

Discovering stages in web navigation for problem-oriented navigation support

Users of web sites often do not know exactly which information they are looking for nor what the site has to offer. The purpose of their interaction is not only to fulfill but also to articulate their information needs. In these cases users need to pass through a series of pages before they can use the information that will eventually answer their questions. Current systems that support navigation predict which pages are interesting for the users on the basis of commonalities in the contents or the usage of the pages. They do not take into account the order in which the pages must be visited. In this paper we propose a method to automatically divide the pages of a web site on the basis of user logs into sets of pages that correspond to navigation stages. The method searches for an optimal number of stages and assigns each page to a stage. The stages can be used in combination with the pages’ topics to give better recommendations or to structure or adapt the site. The resulting navigation structures guide the users step by step through the site providing pages that do not only match the topic of the user’s search, but also the current stage of the navigation process.

Discovering stages in web navigation

Users of web sites often do not know exactly what they are looking for or what the site has to offer. During navigation they use the information found so far to formulate their information needs and refine their search. In these cases users need to pass through a series of pages before they can use the information that will eventually answer their question. Recommender systems aimed at leading users to target pages directly do not provide optimal assistance to these users. In this paper we propose a method to automatically divide web navigation into a number of stages. A recommender can use these stages to recommend pages which do not only match the topic of a users search, but also the current stage of the navigation process. As these recommendations are more tailored toward the users current situation, they can provide better assistance than recommendations made by traditional recommender systems.

Greedy recommending is not always optimal

Recommender systems suggest objects to users. One form recommends documents or other objects to users searching information on a web site. A recommender system can use data about a user to recommend information, for example web pages. Current methods for recommending are aimed at optimising single recommendations. However, usually a series of interactions is needed to find the desired information.

SiteGuide: A tool for web site authoring support

We present ‘SiteGuide’, a tool that helps web designers to decide which information will be included in a new web site and how the information will be organized. SiteGuide takes as input URLs of web sites from the same domain as the site the user wants to create. It automatically searches the pages of these example sites for common topics and common structural features. On the basis of these commonalities it creates a model of the example sites. The model can serve as a starting point for the new web site. Also, it can be used to check whether important elements are missing in a concept version of the new site. Evaluation shows that SiteGuide is able to detect a large part of the common topics in example sites and to present these topics in an understandable form to its users. First results of a user study indicate that Siteguide helps users to create web site designs with better structured contents and links.

Clustering objects from multiple collections

Clustering methods cluster objects on the basis of a similarity measure between the objects. In clustering tasks where the objects come from more than one collection often part of the similarity results from features that are related to the collections rather than features that are relevant for the clustering task. For example, when clustering pages from various web sites by topic, pages from the same web site often contain similar terms. The collection-related part of the similarity hinders clustering as it causes the creation of clusters that correspond to collections instead of topics. In this paper we present two methods to restrict clustering to the part of the similarity that is not associated with membership of a collection. Both methods can be used on top of standard clustering methods. Experiments on data sets with objects from multiple collections show that our methods result in better clusters than methods that do not take collection information into account.