Hashtag Recommendation System in a P2P Social Networking Application
Bibliographic record
Abstract
In this paper focus is on developing a hashtag recommendation system for an online social network application with a Peer-to-Peer infrastructure motivated by BestPeer++ architecture and BATON overlay structure.A user may invoke a recommendation procedure while writing the content.After being invoked, the recommendation procedure returns a list of candidate hashtags, and the user may select one hashtag from the list and embed it into the content.The proposed approach uses Latent Dirichlet Allocation (LDA) topic model to derive the latent or hidden topics of different content.LDA topic model is a well-developed data mining algorithm and generally effective in analysing text documents with different lengths.The topic model is used to identify the candidate hashtags that are associated with the texts in the published content through their association with the derived hidden topics.The experiments for evaluating the recommendation approach were fed with the tweets published in Twitter.Hit-rate of recommendation is considered as an evaluation metricfor our experiments.Hit-rate is the percentage of the selected or relevant hashtags contained in candidate hashtags.Our experiment results show that the hit-rate above 50% is observed when we use a method of recommendation approach independently.Also, for the case that both similar user and user preferences are considered at the same time, the hit-rate improved to 87% and 92% for top-5 and top-10 candidate recommendations respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".