Purchase Intent Forecasting with Convolutional Hierarchical Transformer Networks
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
Purchase intent forecasting, which aims to model user consumption behavior over different categories of items, plays a key role in many services, like online retailing systems, computational advertising and personalized recommendations. While the recently emerged deep neural network models (e.g., recurrent neural network, or attention mechanism) have been proposed to understand user's sequential behavior, we argue that the successes of these methods is largely rely on the data sufficiency. However, the practical purchase forecasting scenarios involve highly sparse data distributions across categories and time. In such cases, one has to deal with the data imbalance problem in order to encode the complex patterns of user purchase behaviors. To tackle this challenge, we develop a Convolutional Hierarchical TRansformer networks (CHTR), to enable the purchase pattern modeling with the multi-grained temporal dynamics, so as to alleviate the data imbalance issue. In our CHTR framework, we develop a multi-grained hierarchical transformer network, to make the learned behavior embeddings be reflective of the multi-level relational structures. Then, a dependency modeling component is proposed to aggregate the multi-relational context signals and capture the underlying dependent structures. Our experiments on real-world datasets show the significant improvements obtained by CHTR over different types of alternative methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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