most citedText-based Emotion Aware Recommender

7 citations · 7 across the 4 of their papers we have counts for

collaborators

6 papers

cs.IR2021

An Affective Aware Pseudo Association Method to Connect Disjoint Users Across Multiple Datasets -- An Enhanced Validation Method for Text-based Emotion Aware Recommender

John Kalung Leung, Igor Griva, William G. Kennedy

We derive a method to enhance the evaluation for a text-based Emotion Aware Recommender that we have developed. However, we did not implement a suitable way to assess the top-N rec…

cs.IR2021

Applying the Affective Aware Pseudo Association Method to Enhance the Top-N Recommendations Distribution to Users in Group Emotion Recommender Systems

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 th…

cs.IR2020

Making Cross-Domain Recommendations by Associating Disjoint Users and Items Through the Affective Aware Pseudo Association Method

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 the…

cs.LG2020

Unsupervised Selective Manifold Regularized Matrix Factorization

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 factoriza…

cs.IR20207 cited

Text-based Emotion Aware Recommender

John Kalung Leung, Igor Griva, William G. Kennedy

We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platfo…

cs.IR2020

Using Affective Features from Media Content Metadata for Better Movie Recommendations

John Kalung Leung, Igor Griva, William G. Kennedy

This paper investigates the causality in the decision making of movie recommendations through the users' affective profiles. We advocate a method of assigning emotional tags to a m…