Heterogeneous Graph-Neural-Network with TimeSequence Information Integration
Bibliographic record
Abstract
Abstract Recommendation systems, as a kind of information filtering system, can understand users' interests based on their personal data or historical behavior records, and are widely used in Web applications such as e-commerce, search, and streaming websites. Based on the fact that users with similar preferences may be interested in similar items, most existing recommendation methods mine this synergistic information through user-item interactions, called collaborative filtering. Recent interaction information can better reflect users' dynamic interests over time. Another group of work is called temporal-based recommendation, which takes into account temporal information to model the user's dynamic interests. A temporal recommendation system is based on the user's temporal interaction data as a context to predict which item the user is most likely to interact with next time. Markov chain-based recommendation models are a typical example of temporal sequential recommendation, which assume that the next interaction is only related to the previous one. Recently, much work has used recurrent neural networks to model previous behaviors and use hidden states to predict the next behavior. However, almost all temporal recommendation methods model user embeddings based only on their own temporal interaction history, ignoring the heterogeneous information widely available in recommender systems, such as product attributes, and encounter cold-start problems when data are sparse and users have fewer interactions. In this paper we proposed a model, the heterogeneous dynamic graph neural network. This network uses a static graph encoder to process the node representation of each heterogeneous graph at different time steps, which includes the node attribute information and edge information, to capture the changing node information in all time steps of the dynamic graph and obtain a valid node representation at each time step. The long and short-term memory model is then used to aggregate the temporal information between different time steps and to mine the connections and interdependencies of different types of nodes between different time steps. The goal of the heterogeneous dynamic graph neural network is to capture the changing node representations of the same nodes as well as different nodes at different time steps in the graph network, expecting to better perform the task of node classification in dynamic graphs. In this paper, we validate the model of heterogeneous dynamic graph neural networks from data, experimenting with node classification tasks on real graph structured data including social networks, e-commerce networks and business review networks, and show that heterogeneous dynamic graph neural networks outperform the latest representation learning methods for static and dynamic graphs.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".