Information retrieval for children: Search Behavior and Solutions

I have been awarded the degree of Phd (Cum Laude) after successfully defending my PhD thesis at the University of Twente on February 14, 2014.

The first contribution of this thesis provides a characterization, on a large scale, of the search behavior of young users. The problems they face when they search for information on the web, the topics they searched and the online activities that motivate search were explored in detail and contrasted against the search behavior of adult users. The results presented in this thesis have important implications for the development of search tools for young users and for the design of educational literacy. Two central problems were identified in the search process of young users: (1) difficulty representing the information needs with keyword queries, and (2) difficulty exploring the list of results.

We found that focused queries are often required to access high quality content for young user with modern search engines. However, young users were found to submit queries that lack the specificity needed to retrieve content that is suitable for them, which leads to frustration during the search process. This observation motivates the second contribution of this thesis. We propose novel query recommendation methods to improve the chances of young users to find content that is suitable and on topic. Concretely, we present an effective biased random walk based on informa- tion gain metrics. This method is combined with topical and specialized features designed for the information domain of young users. We show that our query suggestions outperform by a larger margin not only related query recommendation methods but also the query suggestions offered by the search services available today.

In respect to the second difficulty, it was found that young users have a strong click bias, in which results ranked at the bottom of the result list are rarely clicked. This behavior greatly hampers their navigational skills and exploration of results. It also reduces the chances of young users to find suitable information, since appropriate content for this audience is ranked, on average, at lower positions in the result list in comparison to the content aimed at the average web user.

The third contribution of this thesis aims at helping young users to im- prove their chances to find appropriate content and to ease the exploration of results. For this purpose, we envisage an aggregated search system in which parents, teachers and young users add search services with con- tent of interests for young audiences. We propose a test collection with a wide number of verticals with moderated content, a carefully selected set of search queries and vertical relevant judgments. We also provide novel methods of vertical selection in this information domain based on social media and based on the estimation of the amount of content that is appropriate for young users in each vertical. We show that our methods outperform state-of-the-art vertical selection methods in this information domain. We also show in a case study with children aged 9 to 10 years old that result pages derived from the collection proposed are preferred over the result pages provided by modern search engines. We provide evidence showing that the interaction and exploration of results are improved with result pages built using this collection, even if the users of this case study were unaware between the differences between the types of pages displayed to them.

This thesis is concluded by providing concrete follow-up research directions and by suggesting other information domains that can potentially benefit from the methods proposed in the thesis. My thesis is available online here.

Query Recommendation in the Domain of Information for Children

by Sergio Duarte Torres, Djoerd Hiemstra, Ingmar Weber, Pavel Serdyukov. 

Children represent an increasing part of web users. One of the key problems that hamper their search experience is their limited vocabulary, their difficulty to use the right keywords, and the inappropriateness of general-purpose query suggestions. In this journal paper, we expanded the biased random walk introduced in our paper Query recommendation for Children by combining the score of the random walk with topical and language modeling features to emphasize even more the child-related aspects of the query suggestions.

We evaluate our methods using a large query log sample of queries submitted by children (from the Yahoo! Search logs). We show that our method outperforms by a large margin the query suggestions of modern search engines and state-of-the art query suggestions based on random walks.

Published at JASIST, February 2014. Read the paper.