A generalized stochastic block model for recommendation in social rating networks
Bibliographic record
Abstract
The rapidly increasing availability of online social networks and the well-known effect of social influence have motivated research on social-network based recommenders. Social influence and selection together lead to the formation of communities of like-minded and well connected users. Exploiting the clustering of users and items is one of the most important approaches for model-based recommendation. Users may belong to multiple communities or groups, but only a few clustering algorithms allow clusters to overlap. One of these algorithms is the probabilistic EM clustering method, which assumes that data is generated from a mixture of Gaussian models. The mixed membership stochastic block model (MMB) transfers the idea of EM clustering from conventional, non-relational data to social network data. In this paper, we introduce a generalized stochastic blockmodel (GSBM) that models not only the social relations but also the rating behavior. This model learns the mixed group membership assignments for both users and items in an SRN. GSBM can predict the future behavior of users, both the rating of items and creation of links to other users. We performed experiments on two real life datasets from Epinions.com and Flixster.com, demonstrating the accuracy of the proposed GSBM for rating prediction as well as link prediction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".