no code implementations • 4 May 2023 • John Kalung Leung, Igor Griva, William G. Kennedy, Jason M. Kinser, SoHyun Park, Seo Young Lee
Service providers can update users' Affective Indices in memory without saving their privacy data, providing Affective Aware recommendations without compromising user privacy.
no code implementations • 10 Feb 2021 • John Kalung Leung, Igor Griva, William G. Kennedy
We advocate an emotion aware Pseudo Association Method to associate users across different datasets.
no code implementations • 8 Feb 2021 • John Kalung Leung, Igor Griva, William G. Kennedy
Recommender Systems are a subclass of information retrieval systems, or more succinctly, a class of information filtering systems that seeks to predict how close is the match of the user's preference to a recommended item.
no code implementations • 10 Dec 2020 • John Kalung Leung, Igor Griva, William G. Kennedy
This paper utilizes an ingenious text-based affective aware pseudo association method (AAPAM) to link disjoint users and items across different information domains and leverage them to make cross-domain content-based and collaborative filtering recommendations.
no code implementations • 20 Oct 2020 • Priya Mani, Carlotta Domeniconi, Igor Griva
Manifold regularization methods for matrix factorization rely on the cluster assumption, whereby the neighborhood structure of data in the input space is preserved in the factorization space.
no code implementations • 3 Jul 2020 • John Kalung Leung, Igor Griva, William G. Kennedy
We construct MVECs from the movie emotion profiles.
no code implementations • 1 Jul 2020 • John Kalung Leung, Igor Griva, William G. Kennedy
We advocate a method of assigning emotional tags to a movie by the auto-detection of the affective features in the movie's overview.