Learning Fine-Grained User Preference for Personalized Recommendation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Knowledge graphs (KGs) have garnered significant attention in recommender systems as auxiliary information. Most existing studies consider an item as an entity of a KG and utilize graph neural networks to learn item representations. However, two challenges exist regarding these algorithms: 1) they provide recommended results but fail to explain the reason for which they are preferred by users; 2) user vector representations are concentrated in a small area, thus resulting in similar mass recommendations. In this study, we focus on learning fine-grained user preferences (LFUP) via user-item interactions and using KGs that can capture the reason for which users interact with items. Additionally, a personalized recommendation task is achieved by optimizing the distribution of users in the vector space. User preferences are modeled by using historical interaction items pertaining to users and important relations within the KG. Subsequently, information from two views is aggregated to reduce the semantic differences between them. Finally, user preferences are personalized by maximizing the spatial distance between various user representations via contrastive learning. Experiments on public datasets prove that LFUP significantly benefits user-preference modeling and personalized recommendations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it