RQ-Rec: Residual Quantized Hierarchical Preference Modeling for Cross-Domain Recommendation
Bibliographic record
Abstract
Cold-start recommendation, which addresses the challenge of recommending items to new users without sufficient historical interaction data, remains difficult in personalized recommender systems. Cross-domain recommendation methods have gained attention in addressing this issue by transferring user preferences from a source domain to alleviate the cold-start problem in a target domain. Traditional embedding-based approaches typically rely on a one-to-one alignment over continuous user embeddings using overlapping user IDs, which often leads to severe overfitting and limited generalization, particularly when overlapping users between domains are sparse. In this paper, we propose a novel hierarchical recommendation framework specifically targeting the cold-start problem by modeling user preferences as hierarchical interests derived from textual embeddings and Residual Quantized Variational Autoencoders (RQ-VAE). Unlike traditional methods that directly align embeddings, our approach builds a mapping function transferring hierarchical user interest structures from the source to the target domain. This hierarchical mapping significantly enhances generalization, providing a more comprehensive and robust representation of user preferences. Additionally, we employ a generative rewriting mechanism utilizing Large Language Models to refine user-generated reviews into concise, semantically enriched summaries that explicitly highlight user interests. Extensive evaluations on Amazon review datasets demonstrate the effectiveness of our hierarchical preference modeling and generative rewriting approach, outperforming existing embedding-based methods consistently, especially in cold-start and sparsely populated scenarios. Our proposed method thus provides a robust, flexible solution for personalized recommendation tasks in cold-start conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".