Evaluating the Effectiveness of A Suggested Architecture for The Real-Time Social Recommendation System
Bibliographic record
Abstract
With the growth of social media and online network sites, a large number of textual data are continuously generated every day, however, it is a challenging subject to detect, describe and analyze those unstructured and semi-structured textual data since it has the characteristics of interactivity, sociality, and real-time means. Consequently, researchers have proposed several data mining methods that are used for building effective social recommendation systems to enhance user commercial and social activities. In this paper, we evaluated the performance of our developed real-time social recommendation system called ChatWithRec that aims to analyze the user's contextual conversation dynamically, detect the topic, and then match it with a suitable advertisement to increase the accuracy of recommendations. In our evaluation, we utilized a set of textual datasets to test the conversational analysis segment by using a modified Latent Dirichlet Allocation topic modeling method. Besides, we involved Google's Mobile ad network and an adjusted advertisement database (considering only some fields which are, food and travel subjects including booking hotels and flight adverts) as a task-related output action to collect qualitative data and defining the user's behaviors within-subjects' interaction with our system. The results are encouraging and indicate that the system is fast, satisfy users by getting what they seek without interrupting 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 | 0.004 | 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".