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 the 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.

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

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

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

CLASSES THAT I TEACH

CS 361 – Introduction to the Theory of Computation

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

CS 497 – Special Topics - Introduction to Information Retrieval

CS 481 – CS Seminar

Spring 2017, Fall 2018

CS 568 – Information Retrieval

Spring 2015, Fall 2017

William Bigirimana (B.Sc.)

David McNeill (B.Sc.)

Daniel Bakyono (B.Sc.)

Jason Hall (M.Sc.)

Oghenemaro Anuyah (M.Sc.)

Jennifer Ekstrand (M.Sc.)

Ion Madrazo Azpiazu (Ph.D.)

Web Environment for Children

Designing modules to enhance web search environment tailored towards children by developing query suggestions and query recommendation modules, together with a search environment that filters resources by the readability level of each young user.

Readability Assessment

Multilingual Readability Assessment based on the well known supervised learning techniques from the machine learning field, it makes use of hundreds of features created using Natural Language Processing methods for predicting the level of complexity of an input text.

Recommendation Systems

Recommendation-based systems in the news domain.

RECENT PUBLICATIONS

Maria Soledad Pera, & 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, & 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, & 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, & 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, & 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 & 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.

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.

Hoda Mehrpouyan, Ion Madrazo Azpiazu & 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 & 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 & 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 & 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, & 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 & 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 & 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, & 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, & 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, & 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, & 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 & 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 & 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 & 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 & 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

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.

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:

Ion Madrazo Azpiazu, Nevena Dragovic, Oghenemaro Anuyah, & 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 & 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, & 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, & 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.

Golden Apple Award: Awarded the 2017 Golden Apple Award for the Graduate College at Boise State University.