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Found 7 papers, 0 papers with code
The Application of Affective Measures in Text-based Emotion Aware Recommender Systems
Service providers can update users' Affective Indices in memory without saving their privacy data, providing Affective Aware recommendations without compromising user privacy.
An Affective Aware Pseudo Association Method to Connect Disjoint Users Across Multiple Datasets -- An Enhanced Validation Method for Text-based Emotion Aware Recommender
We advocate an emotion aware Pseudo Association Method to associate users across different datasets.
Applying the Affective Aware Pseudo Association Method to Enhance the Top-N Recommendations Distribution to Users in Group Emotion Recommender Systems
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.
Making Cross-Domain Recommendations by Associating Disjoint Users and Items Through the Affective Aware Pseudo Association Method
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.
Unsupervised Selective Manifold Regularized Matrix Factorization
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.
Using Affective Features from Media Content Metadata for Better Movie Recommendations
We advocate a method of assigning emotional tags to a movie by the auto-detection of the affective features in the movie's overview.
