Diversity Preference Link Rec Eval

diversity-preference-link-rec-eval

Diversity Preference-Aware Link Recommendation for Online Social Networks — Yin et al. (2022) (arXiv:2205.10689, 2022)

What this evaluates

Evaluates link recommendation models on social networks by measuring how well they predict future connections while respecting individual users' diversity preferences across profile dimensions.

Datasets

• Large-scale social network datasets — total ?; splits: test (-1)

Metrics

• DPMS — range: [0, 1]
  • Custom Diversity Preference Matching Score; exact formula not provided in the excerpt.
• Precision — range: [0, 1]
  • Fraction of recommended friends in the top-k list that are actual positive links.
• Recall — range: [0, 1]
  • Fraction of actual positive links successfully recovered in the top-k recommendations.
• F1 Score (primary) — range: [0, 1]
  • Harmonic mean of Precision and Recall: 2 * (Precision * Recall) / (Precision + Recall).

Input / output format

Input: User profile attributes and existing social graph edges.

Output: Top-k ranked candidate friends for recommendation.

Scoring recipe

def compute_metrics(predictions, gold, k):
    hits = len(set(predictions[:k]) & set(gold))
    prec = hits / k
    rec = hits / len(gold) if len(gold) > 0 else 0
    f1 = 2 * prec * rec / (prec + rec) if (prec + rec) > 0 else 0
    return prec, rec, f1

Common pitfalls

• Metrics are reported for varying k (6 to 14), so precision/recall values are not directly comparable across different k settings.
• DPMS is a custom metric; its exact calculation is not defined in the provided text.

Evidence (verbatim from paper)

Table A.6: Comparison between DPA-LR and State-of-the-art Link Recommendation Methods: $k = 6$ to $k = {14}$

Citation

@misc{yin2022linkrec,
  title={Diversity Preference-Aware Link Recommendation for Online Social Networks},
  author={Yin et al. (2022)},
  year={2022},
  note={arXiv:2205.10689}
}

• arXiv: 2205.10689