Fair Recommendations for Online Barter Exchange Networks
Bibliographic record
Abstract
Of late online social networks have become popular, with interest spanning various aspects including search, analysis/mining, and their potential use for item barter exchange markets. The idea is that users can leverage their social network for exchanging items they possess with other users. The problem of generating recommendations for item exchanges between users, consisting of synchronous exchange cycles has been investigated[2]. In this paper, we identify the shortcomings of the above exchange model and propose an asynchronous model that makes use of credit points. Rather than insist on exchanging items synchronously, we award points to users whenever they give items to other users, which can be redeemed later. Points and their redemption raise an issue of fairness which intuitively means users who contribute more should have a greater priority over others for receiving items they wish for. We focus on fairness maximization and prove that it is NPhard and cannot be approximated within any factor in polynomial time unless P=NP. We then develop efficient heuristic algorithms, and experimentally demonstrate their effectiveness and scalability on both synthetic data and a real dataset from readitswapit.co.uk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".