IPARS: An Image-based Personalized Advertisement Recommendation System on Social Networks
Why this work is in the frame
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Bibliographic record
Abstract
Social media has become a primary source of information for decision-makers, organizations, and scientists in today’s fast-paced world. Indeed, because of the large volume of user-generated data available on social media, these online platforms are viewed as computable data sources that potentially mirror reality. It could be an authentic environment for the task of target customers identification for marketing. This paper presents a novel image-based personalized advertisement recommendation system named IPARS to identify target customers in social media using image processing and machine learning techniques for an online advertisement. Assume having a set of advertising images; The problem is identifying a group of social media users who are likely to be the potential target of those images. In IPARS, a given social network is first converted into a weighted bipartite graph where the nodes are the users and keywords. Then, another bipartite graph is formed by decomposing the advertising images into their objects, labels, concepts, and sentiments. We propose a couple of formulas to calculate the weight of edges for both graphs. An algorithm is proposed to search the social graph and identify and rank the best group of users. We have evaluated our proposed model on a set of images and users from the Flickr dataset and Twitter. The results showed that IPARS has a better performance compared with other algorithms.
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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 | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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