Papers by Reinald Adrian Pugoy
Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering (2021.acl-long)
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| Challenge: | Existing extractive summarization-based collaborative filtering models learn accurate representations of users and items based on user-given numeric ratings, but employing them is an oversimplification of user preferences and item characteristics. |
| Approach: | They propose to use BERT, K-Means embedding clustering, and multilayer perceptron to learn sentence embeddations, representation-explanations, and user-item interactions to create extractive summaries. |
| Outcome: | The proposed model improves rating prediction accuracy and user/item explainability. |
BERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation (2020.aacl-main)
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| Challenge: | Existing models that learn accurate representations of users and items are based on ratings, which oversimplify user preferences and item characteristics. |
| Approach: | They propose a novel, accurate, and explainable recommender model that integrates three key elements: BERT, multilayer perceptron, and maximum subarray problem to derive contextualized review features, model user-item interactions, and generate explanations. |
| Outcome: | The proposed model outperforms state-of-the-art models by an improvement gain of nearly 7% based on the human judges’ assessment . |