FCRC: A Fully Connected Recurrent Convolutional Network for Recommendation
Bibliographic record
Abstract
In pursuit of superior recommendation performance, it is compulsory to leverage information that goes beyond simple user-item interaction histories. Traditional supervised learning recommenders, like factorization machines (FM), collect both userand item attributes to enhance their recommendation while treating each interaction as an independent data instance. Later research showed that interaction histories can be effectively organized into a knowledge graph (KG), thereby exploiting the data’s spatial information and interconnectedness. Although these KG-equipped methods are capable of superior performance, the advent of KGs, caused prevalent literature to bifurcate into item or user attribute models. By exploiting only a single knowledge domain, such models forego additional performance. This work combines both knowledge domains into a single Fully Connected Recurrent Convolutional Neural Network (FCRC). Specifically, a method for concurrently leveraging both knowledge domains into a single graph is presented, alongside a novel recurrent pooling approach for improved message passing throughout the graph.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".