Finding, Understanding and Learning: Making Information Discovery Tasks Useful for Children and Teachers

We present our ongoing efforts on the development of a search environment tailored to 6-15 year-olds that can foster learning though retrieval of materials that not only satisfy the information needs of users but also match their reading abilities. YouUnderstood.me is an enhanced environment based on a popular search engine specifically designed to help students deal with search for learning tasks, and allow teachers to track their progress. An initial assessment conducted on YouUnderstood.me and well-known (children-oriented) search engines based on queries generated by K-9 students, showcases the need for this type of environment

Extending Safe Search Functionality for Identifying Child-Safe and Educational Web Resources

Safe search is a filtering strategy used by search engines for the purpose of preventing children from accessing web resources that either contain adult content (i.e., pornography and nudity) or promote violence (i.e., include hate-speech and offensive language). Unfortunately, safe search is not always the perfect deterrent: at times, pornographic and hate-based resources slip through the filter, whereas, other times, resources that may be relevant to a child’s educational search context are misconstrued as being inappropriate, and are therefore filtered. In this paper, we first examine the functionality of a number of existing safe search filters. Based on our findings, we present ongoing efforts to address some of the limitations with traditional safe search filtering strategies

From Recommendation to Curation: When the System Becomes Your Personal Docent

Curation is the act of selecting, organizing, and presenting content. Some applications emulate this process by turning users into curators, while others use recommenders to select items, seldom achieving the focus or selectivity of human curators. We bridge this gap with a recommendation strategy that more closely mimics the objectives of human curators. We consider multiple data sources to enhance the recommendation process, as well as the quality and diversity of the provided suggestions. Further, we pair each suggestion with an explanation that showcases why a book was recommended with the aim of easing the decision making process for the user. Empirical studies using Social Book Search data demonstrate the effectiveness of the proposed methodology

Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and Users

In this paper, we consider the promise and challenges of deploying recommendation and information retrieval technology to help teachers locate resources for use in classroom instruction. The classroom setting is a complex environment presenting a number of challenges for recommendation, due to its inherent multi-stakeholder nature, the multiple objectives that quality educational resources and experiences must simultaneously satisfy, and potential disconnect between the direct user of the system and the end users of the resources it provides. In this paper, we outline these challenges, highlight opportunities for new research, and describe our work in progress in this area including insights from interviews with working teachers

Is Cross‐Lingual Readability Assessment Possible?

Most research efforts related to automatic readability assessment focus on the design of strategies that apply to a specific language. These state‐of‐the‐art strategies are highly dependent on linguistic features that best suit the language for which they were intended, constraining their adaptability and making it difficult to determine whether they would remain effective if they were applied to estimate the level of difficulty of texts in other languages. In this article, we present the results of a study designed to determine the feasibility of a cross‐lingual readability assessment strategy. For doing so, we first analyzed the most common features used for readability assessment and determined their influence on the readability prediction process of 6 different languages: English, Spanish, Basque, Italian, French, and Catalan. In addition, we developed a cross‐lingual readability assessment strategy that serves as a means to empirically explore the potential advantages of employing a single strategy (and set of features) for readability assessment in different languages, including interlanguage prediction agreement and prediction accuracy improvement for low‐resource languages

Hierarchical Mapping for Crosslingual Word Embedding Alignment

The alignment of word embedding spaces in different languages into a common crosslingual space has recently been in vogue. Strategies that do so compute pairwise alignments and then map multiple languages to a single pivot language (most often English). These strategies, however, are biased towards the choice of the pivot language, given that language proximity and the linguistic characteristics of the target language can strongly impact the resultant crosslingual space in detriment of topologically distant languages. We present a strategy that eliminates the need for a pivot language by learning the mappings across languages in a hierarchicalway. Experiments demonstrate that our strategy significantly improves vocabulary induction scores in all existing benchmarks, as well as in a new non-English–centered benchmark we built, which we make publicly available

Measuring Personality for Automatic Elicitation of Privacy Preferences

The increasing complexity and ubiquity in user connectivity, computing environments, information content, and software, mobile, and web applications transfers the responsibility of privacy management to the individuals. Hence, making it extremely difficult for users to maintain the intelligent and targeted level of privacy protection that they need and desire, while simultaneously maintaining their ability to optimally function. Thus, there is a critical need to develop intelligent, automated, and adaptable privacy management systems that can assist users in managing and protecting their sensitive data in the increasingly complex situations and environments that they find themselves in. This work is a first step in exploring the development of such a system, specifically how user personality traits and other characteristics can be used to help automate determination of user sharing preferences for a variety of user data and situations. The Big-Five personality traits of openness, conscientiousness, extroversion, agreeableness, and neuroticism are examined and used as inputs into several popular machine learning algorithms in order to assess their ability to elicit and predict user privacy preferences. Our results show that the Big-Five personality traits can be used to significantly improve the prediction of user privacy preferences in a number of contexts and situations, and so using machine learning approaches to automate the setting of user privacy preferences has the potential to greatly reduce the burden on users while simultaneously improving the accuracy of their privacy preferences and security

Is Sven Seven? : A Search Intent Module for Children

The Internet is the biggest data-sharing platform, comprised of an immeasurable quantity of resources covering diverse topics appealing to users of all ages. Children shape tomorrow\u27s society, so it is essential that this audience becomes agile with searching information. Although young users prefer well-known search engines, their lack of skill in formulating adequate queries and the fact that search tools were not designed explicitly with children in mind, can result in poor outcomes. The reasons for this include children\u27s limited vocabulary, which makes it challenging to articulate information needs using short queries, or their tendency to create queries that are too long, which translates to few or irrelevant retrieved results. To enhance web search environments in response to children\u27s behaviors and expectations, in this paper we discuss an initial effort to verify well-known issues, and identify yet to be explored ones, that affect children in formulating (natural language or keyword) queries. We also present a novel search intent module developed in response to these issues, which can seamlessly be integrated with existing search engines favored by children. The proposed module interprets a child\u27s query and creates a shorter and more concise query to submit to a search engine, which can lead to a more successful search session. Initial experiments conducted using a sample of children queries validate the correctness of the proposed search intent module

Scripts for Can We Leverage Rating Patterns from Traditional Users to Enhance Recommendations for Children?

This archive contains the scripts to replicate the experiment for our paper [ Can we leverage rating patterns from traditional users to enhance recommendations for children? published in ACM RecSys by - Ion Madrazo Azpiazu, Michael Green, Oghenemaro Anuyah, and Maria Soledad Pera.] ### Requirements - Java - An R, Jupyter, Python, and Tidyverse installation. - The [MovieLens 1M](https://grouplens.org/datasets/movielens/) dataset, extracted into Data/input (you should have directory Data/ml-1m) - The Dogo dataset (or any dataset containing ratings provided by children) extracted into Data/input ### Instructions - Steps to run: - Install required software and data files enlisted in requirements. These directories and files should be present upon doing so: -Data/input/ml-1m (e.g. data/ml-1m/ratings.dat) -Data/input/Dogo (e.g., any children ratings file) - Run Jupyter notebook: - Create_Experimental_Datasets/Data_creation_notebook.ipynb - Run LibReC Experiment: - Input_Analysis/ - Visualize user-rating activity: - Rating_Distribution_Dogo_ML1M.ipynb ### On October 9, 2018 the downloaded zip file was revised to remove some lines of code that were intended for internal analyses only