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Improvising diversity of personalized recommendation systems

Recent Research papers:

• Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques:

  107 Citations : IEEE Transactions on Knowledge and Data Engineering
  we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms

• Recommendation Diversification Using Explanations: (Data Engineering, 2009. ICDE '09. IEEE 25th International Conference)

Traditionally, the problem is addressed through attribute-based diversification grouping items in the result set that share many common attributes (e.g., genre for movies) and selecting only a limited number of items from each group. It is, however,

zyfnhct

#!/bin/bash
yarn application -list | awk '{print $1}' | while read line;do
    echo $line
    yarn application -kill $line
done