Targeted content dissemination in mobile social networks taking account of resource limitation
Bibliographic record
Abstract
Summary Mobile social networks are presenting new opportunities for content dissemination. User location, mobility, and social communities can be used to deliver delay‐tolerant content. Most existing dissemination methods either fail to consider the user's interest in their protocol design or just allow exchange of contents between nodes that have a similar interest. If nodes with similar interest never encounter each other, then contents might not be exchanged with them. In this paper, we propose a method in which user location, mobility, and interest as well as certain limiting factors such as relay buffer size and communication overhead are used to select a set of relays for targeted advertisement distribution. Besides, our method does not depend on nodes with similar interest encountering one another. In the proposed method, a distribution agent exploits user location, mobility, and social networks as well as the interest of destinations to select a group of relays to carry content to the targeted destinations. Users move among social communities and carry advertisements to their peers in other communities. We have also developed an optimization problem for advertisement selection and scheduling. Since the problem is NP‐hard, we propose a heuristic solution. Evaluation of the proposed approach shows that the algorithm is not sensitive to network resource variation and readily outperforms popular methods reported in the literature in terms of delay, delivery ratio, and interest compatibility.
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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 | 0.002 |
| 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".