Skip to Main Content
Mobile Menu

Papers

Exploiting Reviews to Generate Personalized and Justified Recommendations to Guide Users’ Selections

Nevena Dragovic and Sole Pera. 2017. “Exploiting Reviews to Generate Personalized and Justified Recommendations to  Guide Users’ Selections”.  To appear in Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. Abstract:  We introduce RUS, a recommender that assists users … (Read More) Exploiting Reviews to Generate Personalized and Justified Recommendations to Guide Users’ Selections

UBR: A Book Search–Recommender Hybrid

Jason Hall and Sole Pera. 2017. “UBR: A Book Search–Recommender Hybrid” To appear in Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. Abstract:  We present UBR, a Unit Book Recommender that combines search and recommendation into a … (Read More) UBR: A Book Search–Recommender Hybrid

Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation

Michael D. Ekstrand and Vaibhav Mahant. 2017. “Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation”. To appear in Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. Abstract Top-N evaluation of recommender systems, typically carried out … (Read More) Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation

Recommender Response to Diversity and Popularity Bias in User Profiles

Sushma Channamsetty and Michael D. Ekstrand. 2017. “Recommender Response to Diversity and Popularity Bias in User Profiles”. Short paper in Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. Abstract Recommender system evaluation usually focuses on the over-all … (Read More) Recommender Response to Diversity and Popularity Bias in User Profiles