Toward A Real-Time Social Recommendation System
Bibliographic record
Abstract
Recent research has investigated approaches and models to produce optimal results in social recommendation systems (SRSs) particularly in text-based form. The aim is to analyze the user generated-content (UGC) to suggest appropriate recommendations to interested users. However, users are often not satisfied with the initial recommendations because some models do not elicit their preferences at the beginning of the interaction nor do they understand their actual needs. In this paper, we propose a real-time SRSs called ChatWithRec that aims to improve the accuracy of recommendations by analyzing the user's contextual conversation dynamically, detect the topic, and then match it with a suitable advertisement. We used the Latent Dirichlet Allocation topic model (LDA) to analyze the user's conversation and perceive topics. We evaluated our system by applying several metrics like coherence, and F-score to evaluate the performance of ChatWithRec recommendation system. The results are encouraging, indicating that the system is fast, satisfies users by getting exactly what they seek in their conversation flow.
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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 | 0.001 |
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 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".