Sole Pera, Ph.D.

Boise State University | solepera@boisestate.edu

ABOUT

After completing her Ph.D. in Computer Science at Brigham Young University in 2014, Dr. Maria Soledad Pera, a native of Argentina, joined Boise State University as an Assistant Professor in the Department of Computer Science, where she currently co-leads the People and Information Research Team (Piret). Sole's main area of expertise is in information retrieval and her current research work focuses on the application of information retrieval, information extraction, and natural language processing techniques for developing search and recommendation systems, primarily for children.

Research Interest: Information Retrieval, Web Search, Recommender Systems, Natural Language Processing, Machine Learning, Information Extraction, Databases, System Analysis.

EDUCATION

PhD in Computer Science
Brigham Young University, Provo, Utah, USA
Proposed Dissertation Topic: “Using Online Data Sources to Make Recommendations on Reading Materials for K-12 and Advanced Readers” – Advisor: Dr. Yiu-Kai Ng

April 2014

M.S. in Computer Science
Brigham Young University, Provo, Utah, USA
Thesis: “Improving Library Searches Using Word-Correlation Factors and Folksonomies” – Advisor: Dr.Yiu-Kai Ng

April 2009

B.S. in Information Systems Analysis
Universidad Tecnologica Nacional, Rosario, Argentina

September 2006

CLASSES THAT I TEACH

CS 361 – Introduction to the Theory of Computation

Fall 2014, Fall 2015, Spring 2016, Summer 2016, Spring 2017, Spring 2018, Fall 2018, Spring 2019

CS 537 – Introduction to Information Retrieval

Fall 2016

CS 481 – CS Seminar

Spring 2017, Fall 2018

CS 637 – Information Retrieval

Spring 2015, Fall 2017

RECENT PUBLICATIONS

Nevena Dragovic, Ion Madrazo Azpiazu, and Maria Soledad Pera. “From Recommendation to Curation: When the system becomes your personal docent". In: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS); co-located with ACM Conference on Recommender Systems, Vancouver, Canada, 2018.

Michael Ekstrand, Ion Madrazo Azpiazu, Katherine Landau Wright, and Maria Soledad Pera. “Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and Users”. In: Proceedings of the 2nd Workshop on Recommendation in Complex Scenarios; co-located with ACM Conference on Recommender Systems, Vancouver, Canada, 2018.

Ion Madrazo Azpiazu, Michael Green, Oghenemaro Anuyah, and Maria Soledad Pera. “Can we leverage rating patterns from traditional users to enhance recommendations for children?”. In: Poster Proceedings of the 12th ACM Conference on Recommender Systems (RecSys '18), Vancouver, Canada, 2018.

Anu Shresta, Francesca Spezzano, and Maria Soledad Pera. “Who is Really Affected by Fraudulent Reviews?: An analysis of shilling attacks on recommender systems in real-world scenarios”. In: Poster Proceedings of the 12th ACM Conference on Recommender Systems (RecSys '18), Vancouver, Canada, 2018.

Yiu-Kai Ng and Maria Soledad Pera. “Recommending Social-Interactive Games for Adults with Autism Spectrum Disorders (ASD)”. In: Proceedings of the ACM Conference on Recommender Systems (pp. 209-213), Vancouver, Canada, 2018.

Oghenemaro Anuyah, Jerry Alan Fails, and Maria Soledad Pera. “Investigating Query Formulation Assistance for Children”. In: Proceedings of the 17th ACM Conference on Interaction Design and Children (pp. 581-586), Trondheim, Norway, 2018.

Maria Soledad Pera, Katherine Wright, and Michael D. Ekstrand. "Recommending Texts to Children with an Expert in the Loop". In Proceedings of the 2nd International Workshop on Children and Recommender Systems (Kidrec), IDC 2018, Trondheim, Norway, June, 2018.

Katherine Wright, Michael D. Ekstrand, and Maria Soledad Pera. "Supplementing Classroom Texts with Online Resources: Challenges and Possibilities". To be presented at the Annual Meeting of the Northwest Rocky Mountain Educational Research Association, Salt Lake City, UTAH, 2018. (25-Minute Research Presentation).

Jerry Alan Fails, Maria Soledad Pera, Natalia Kucirkova, and Franca Garzotto. "International and interdisciplinary perspectives on children and recommender systems (KidRec)". In Proceedings of the 17th ACM Conference on Interaction Design and Children, pp. 705-712. ACM, 2018.

Maria Soledad Pera, Jerry Alan Fails, Mirko Gelsomini, and Franca Garzotto. "Building Community: Report on KidRec Workshop on Children and Recommender Systems at RecSys 2017". SIGIR Forum, (Vol. 52, No. 1, pp. 153–161), 2018.

Maria Soledad Pera, and Yiu-Kai Ng. “Recommending Books to be Exchanged Online in the Absence of Wish Lists”. In Journal of the Association for Information Science and Technology (Vol. 69, No. 4, pp. 541–552), 2018.

Ion Madrazo Azpiazu, Nevena Dragovic, Oghenemaro Anuyah, and Maria Soledad Pera. “Looking for the Movie Seven or Sven from the Movie Frozen? A Multi-perspective Strategy for Recommending Queries for Children”. In Proceedings of the Conference on Human Information Interaction and Retrieval (CHIIR) (pp. 92-101), New Brunswick, 2018. | DATASET

Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. “All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness”. In Proceedings of the Machine Learning research in the Fairness, Accountability and Transparency Conference (pp 172-186), New York, 2018. | SCRIPTS

Maria Soledad Pera, Jerry Alan Fails, Mirko Gelsomini, and Franca Garzotto. “Building Community: Report on KidRec Workshop on Children and Recommender Systems at RecSys 2017”. In ACM RecSys, 2017.

Maria Soledad Pera, and Yiu-Kai Ng. “Using Online Data Sources to Make Query Suggestions for Children”. In Journal of Web Intelligence (Vol. 15, No. 4, pp. 303-323), IOS Press, 2017.

Ion Madrazo Azpiazu, Maria Soledad Pera, Nevena Dragovic, and Jerry Alan Fails. “Online Searching and Learning YUM and Other tools for Children and Teachers”. In Information Retrieval Journal (Vol. 20, No. 5, pp. 524-545), 2017.

Oghenemaro Anuyah, Ion Madrazo Azpiazu, David McNeill and Maria Soledad Pera. “Can Readability Enhance Recommendations on Community Question Answering Sites?”. In Proceedings of the ACM Conference on Recommender Systems (ACM RecSys) - Poster, Como, Italy, August 2017.

Ion Madrazo Azpiazu, Guadalupe Guereta, Santiago Diez, Martin Bianculli Martin, Alejandro Gonzalez, Carlos Flury, Franco Ferrari, Mariano Baudena, Guillermo Leale, and Maria Soledad Pera. “Comparative analysis on text distance measures applied to Community Question Answering data”. Congreso Nacional de Ingenieria Informatica (CONAIISI), 2017.

Michael D. Ekstrand and Maria Soledad Pera. "The Demographics of Cool: Popularity and Recommender Performance for Different Groups of Users". In Proceedings of the ACM Conference on Recommender Systems (ACM RecSys) - Poster, Como, Italy, August 2017.

Deepa Mallela, Dirk Ahlers, and Maria Soledad Pera. “Mining Twitter Features for Event Summarization and Rating”. In Proceedings of the International Conference on Web Intelligence (pp. 615-622), 2017.

Hoda Mehrpouyan, Ion Madrazo Azpiazu and Maria Soledad Pera. “Measuring Personality for Automatic Elicitation of Privacy Preferences”. In the IEEE Symposium on Privacy-Aware Computing (IEEE PAC) Conference (pp. 84-95), 2017.

Nevena Dragovic and Maria Soledad Pera. “Exploiting Reviews to Generate Personalized and Justified Recommendations to Guide Users' Selections”. In the Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 661-664), 2017.

Jason Hall and Maria Soledad Pera. “UBR: A Book Search - Recommender Hybrid”. In the Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 665-669), 2017.

Sean MacLachlan, Nevena Dragovic, Stacey Donohue and Maria Soledad Pera. "One Size Doesn’t Fit All: Helping Users Find Events from Multiple Perspectives". In Proceedings of the ACM RecSys Workshop on Recommenders in Tourism, Boston, USA, September 2016.

Stacey Donohue, Nevena Dragovic, and Maria Soledad Pera. "Anything Fun Going On? A Simple Wizard to Avoid the Cold-Start Problem for Event Recommenders". In Proceedings of the ACM RecSys Workshop on Recommenders in Tourism, Boston, USA, September 2016.

Ion Madrazo Azpiazu and Maria Soledad Pera. "Is Readability a Valuable Signal for Hashtag Recommendations?". In Proceedings of the ACM Conference on Recommender Systems (ACM RecSys 2016) - Poster, Boston, USA, September 2016.

Nevena Dragovic and Maria Soledad Pera. "Genre Prediction to Inform the Recommendation Process". In Proceedings of the ACM Conference on Recommender Systems (ACM RecSys 2016) - Poster, Boston, USA, September 2016.

Ion Madrazo Azpiazu, Nevena Dragovic, and Maria Soledad Pera. "Finding, Understanding and Learning: Making Information Discovery Tasks Useful for Children and Teachers". In Proceedings of ACM SIGIR Workshop on Search as Learning. Pisa, Italy, July 2016.

Nevena Dragovic, Ion Madrazo Azpiazu, and Maria Soledad Pera. "Is Sven Seven?: A Search Intent Module for Children". In Proceedings of the 39th International ACM SIGIR Conference (pp. 885-888), Pisa, Italy, July 17-21, 2016.

Joel Denning, Maria Soledad Pera, and Yiu-Kai Ng. "A readability level prediction tool for K-12 books". In Journal of the Association for Information Science and Technology (Vol. 67, No. 4, pp. 550–565), 2015.

Meher Shaikh, Maria Soledad Pera, and Yiu-Kai Ng. "Suggesting Simple and Comprehensive Queries to Elementary-Grade Children". In Proceedings of the IEEE/WIC/ACM Conference on Web Intelligence (WI'15) (pp. 252-259), Singapore, December 6-9, 2015.

Nevena Dragovic and Maria Soledad Pera. "Exploiting Reviews to Guide Users’ Selections". In Proceedings of the 9th ACM Conference on Recommender Systems (ACM RecSys 2015) - Poster, Vienna, Austria, September 16-20, 2015.

Maria Soledad Pera and Yiu-Kai Ng. "A Recommendation-Based Book-Exchange System Without Using Wish Lists". In Proceedings of the 9th ACM Conference on Recommender Systems (ACM RecSys 2015) - Poster, Vienna, Austria, September 16-20, 2015.

Shahrzad Karimi and Maria Soledad Pera. "Recommendations to enhance Children Web Searches". In Proceedings of the 9th ACM Conference on Recommender Systems (ACM RecSys 2015) - Poster, Vienna, Austria, September 16-20, 2015.

Maria Soledad Pera and Yiu-Kai Ng. "Analyzing Book-Related Features to Recommend Books for Emergent Readers". In Proceedings of the 26th ACM Conference on Hypertext and Social Media (ACM HT) (pp. 221-230), Cyprus, September 1-4, 2015.

Full Publication List

GRANTS

Child Adaptive Search Tool (CAST)

Abstract: The aim of the project is to empower emergent searchers -- initially children ages 6-11 -- by researching, designing, and developing search tools that improve their information literacy and searching capabilities through modeling and adapting to their abilities as they search. Current search engines, even ones specifically designed for children, offer weak support for children's search needs due to their developing skills related to spelling, language use (including synonyms), understanding categories, refining queries, and evaluating relevance and quality of results. This makes it hard for children to create effective queries, use the results suggested by the search engine, and understand the relationship between the queries and the results returned. Bringing together expertise in human-computer interaction, information retrieval, natural language processing, and education, the project team will both (a) further scientific understanding of children's search abilities, and (b) design tools to support it through the iterative development of CAST (Child Adaptive Search Tool), designed for children aged 6-11. CAST will be designed to model and respond to users' literacy and maturity levels as well as search intent missing from their formal queries. For example, when a child submits the query "Tiger", CAST will tend to prioritize tiger habitat or Winnie the Pooh's friend Tigger, which likely correlate better to a child's search intent than information on Tiger Woods. To reach this goal, the team will collaborate with children and teachers throughout the course of the project, working with partners in several local schools to increase the impact of the application itself and to improve the dissemination of the results. The results on supporting search in the special population of children in this research also have the potential to inform similar problems and methods aimed at other populations who might have systematic differences in their search ability, from older adults to second language speakers.
More information: CHS: Medium: CAST: Child Adaptive Search Tool

LITERATE: Locating Informational Texts for Engaging Readers and Teaching equitably

Project Outline: The goal of this project is to prototype LITERATE (Locating Informational Texts for Engaging Readers and Teaching Equitably), a web based application to help teachers locate news articles to use in upper elementary.

Related Outcomes:
Literate Demo

IR for Children: Enhanced Search Environment for Children ( CRII: III: Children and Information Retrieval Tasks: Search Intent, Query Suggestions, and Adequate Online Resources )

Abstract: Children are introduced to the Web at increasingly young ages. While early exposure can help them build foundational skills vital in a knowledge-rich society, search tools were not designed with children in mind nor do retrieved results explicitly target children. Most engines do not support children's inquiry approaches (or do not support them well) and typically do not return content suitable to children's interests or reading levels. This need is important to address given that early experiences can affect attitudes in using the Web, skill development in making adequate use of resources for personal and educational interests, and the ability to leverage information and use it to make contributions into adulthood. The PI and her team will design and develop software modules as search engine add-ons to meet the needs of children searching the Web. The modules, which will upgrade current computation infrastructure, will be domain-independent, tailored to children, and usable on Google, to locate child-friendly educational- and leisure-related information. Research outputs will facilitate children's engagement with technology by improving their interactions on the Web. Partnerships with Idaho K-9 classrooms will allow the research team to gather feedback from children and teachers and verify the usefulness of the proposed modules in their intended, formal setting.
More information: Working Toward a Better, Kid-Friendly Search Engine

Related Publications:
Oghenemaro Anuyah, Jerry Alan Fails, and Maria Soledad Pera. “Investigating query formulation assistance for children”. In: Proceedings of the 17th ACM Conference on Interaction Design and Children (pp. 581-586), Norway, 2018.

Ion Madrazo Azpiazu, Nevena Dragovic, Oghenemaro Anuyah, and Maria Soledad Pera. “Looking for the movie Seven or Sven from the movie Frozen? A multi-perspective strategy for recommending queries for children”. In: Proceedings of the Conference on Human Information Interaction and Retrieval (CHIIR) (pp. 92-101), New Brunswick, 2018.

Ion Madrazo Azpiazu, Maria Soledad Pera, Nevena Dragovic, Jerry Alan Fails. “Online Searching and Learning YUM and Other tools for Children and Teachers”. Information Retrieval Journal (2017).

Oghenemaro Anuyah, Ion Madrazo Azpiazu, David McNeill and Maria Soledad Pera. “Can Readability Enhance Recommendations on Community Question Answering Sites?”. In Proceedings of the ACM Conference on Recommender Systems (ACM RecSys 2017) - Poster, Como, Italy, August 2017.

Ion Madrazo Azpiazu, Nevena Dragovic, and Maria Soledad Pera. "Finding, Understanding and Learning: Making Information Discovery Tasks Useful for Children and Teachers". In Proceedings of ACM SIGIR Workshop on Search as Learning. Pisa, Italy, July 2016.

Nevena Dragovic, Ion Madrazo Azpiazu, and Maria Soledad Pera. "Is Sven Seven?: A Search Intent Module for Children". In Proceedings of the 39th International ACM SIGIR Conference, Pisa, Italy, July 17-21, 2016.