Papers with neural collaborative filtering
BERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation (2020.aacl-main)
Copied to clipboard
| 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 . |
Neural Conversation Recommendation with Online Interaction Modeling (D19-1)
Copied to clipboard
| Challenge: | Existing models that only use lexical features and ignore past user interactions in online conversations are inadequate to identify and engage in online discussions. |
| Approach: | They propose a framework that automatically recommends conversations based on user's prior conversation behaviors by exploring deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. |
| Outcome: | The proposed model outperforms state-of-the-art models on two large-scale datasets from Twitter and Reddit showing that it incorporates deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. |
Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree (2024.emnlp-main)
Copied to clipboard
| Challenge: | Disagreement among annotators can reveal nuances in subjective tasks that lack a simple ground truth . |
| Approach: | They propose three approaches to predict annotator ratings on the toxicity of text . they integrate annotators' history, demographics, survey information into their models . |
| Outcome: | The proposed approach outperforms other methods in toxicity rating prediction. |