Looking for Like-Minded Individuals in Social Networks Using Tagging and E Fuzzy Sets
Bibliographic record
Abstract
The web is perceived as a new social platform. Very often, the users look at the web as a place where they can find an individual or group of people with the same or similar interests, or even find new friends. Such situation is reflected in one of the aspects of the web 2.0 called tagging. Tagging is a process of labeling (annotating) digital items-resources-by users. The labels-tags-assigned to those resources reflect users' ways of seeing, categorizing, and perceiving particular items. In general, a single user can label a number of items with a number of different tags. The results of this activity-labeled items and used tags-can be perceived as information characterizing the user. This paper describes an approach for constructing a user signature representing her interests and opinions based on used items and tags. The signature is determined as a fuzzy relation built on two fuzzy sets proposed here: a fuzzy set representing resource attractiveness, and a fuzzy set representing tag popularity. Furthermore, users' signatures are used to determine similarity between users, and potentially give users a method to find new web friends with similar interests and opinions. The paper also describes a process of building different signatures representing a group of users. Signatures of users that are members of the group are aggregated using OWA operator and different linguistic quantifiers to describe the group in a number of ways. A real-world case study illustrating the process of finding similar users and/or groups of users is included.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".