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Tweet Recommendation with Graph Co-Ranking

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Yoshinari Fujinuma
Yoshinari Fujinuma
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Tweet Recommendation with Graph Co-Ranking Rui Yan, Mirella Lapata, Xiaoming Li ACL 2012 Reader: 東京大学 相澤研究室 藤沼祥成
Motivation • 3 problems related to tweet recommendation – Linkage of following and retweeting – Interest the user – Personalization and diversity
Related Work • Collaborative Filtering [Hannon et al. 2010] • Selecting tweets including URLs [Chen et al. 2010] – And so on… • Co-Ranking Framework: Scientific impact and modeling the relationship between authors and their publications [Zhou et al., 2007].
What is Proposed in this Paper • Adapting Co-Ranking framework to Tweet recommendation • Including personalization
Graphs Tweet-Author Graph Tweet Graph Author Graph
Co-Ranking Algorithm • Simultaneously rank tweets and their authors – a tweet is important if it associates to other important tweets – A user is important if the associate to other important users, and they write important tweets
Components of Co-Ranking • Popularity (PageRank [Brin and Page 1998]) • Personalization (PersRank) – Modifying PageRank • Diversity (DivRank [Mei et al. 2010]) – Avoid assigning only high scores to closely connected nodes – Popular nodes get popular
Popularity: PageRank • (1-μ): stick to the random walk • μ: Jump to any vertex chosen uniformly at random • m: ranking scores of for the vertices in Tweet graph
Personalization (1/2) • Used Latent Dirichlet Allocation to construct the matrix D • Dij: Probabilitiy of tweet mi belongs to topic tj • Image of D Tweets 𝐷11 ⋯ 𝐷1𝑛 Topics ⋮ ⋱ ⋮ 𝐷 𝑚1 ⋯ 𝐷 𝑚𝑛
Personalization (2/2) • r: ri = the probability for a user to respond to tweet mi • Estimate t: topic interest vector by maximum likelihood
Diversity: DivRank • Transition probabilities change over time • Favors popular nodes as time goes by • After z iterations, M is
CoRank: Figure
Actual Steps • Step 1 Walk from the author • Step 2 Walk from the tweet Ensuring convergence
Co-Ranking Algorithm • Coupling parameter λ • If λ=0, no coupling between Tweet graph and Author graph • In experiment, λ = 0.6
Transition Matrix in Author Graph • It is defined as
Transition Matrix in Tweet Graph • Tweet Graph is defined as • • mi a term vector is weighted as tf・idf
Transition Matrix in Tweet- Author Graph • MU: • UM: • : tweet mi is authored by uj
Data Set • 9,449,542 users – Tracing the edges of 23 users’ followers and followees until no new user is added • 3/25/2011 to 5/30/2011 • 364,287,744 tweets
Evaluation • Automatically – Golden: A tweet is retweeted or not • Human-based Judgement – 23 users – Whether they will retweet or not – Calculating the mean
Baselines • Randomly ranked (Random) • Longer tweets ranked higher (Length) • Many retweets ranked higher (RTnum) • RankSVM algorithm (RSVM) [Duan et al. 2010] • Decision Tree Classifier (DTC) [Uysal and Croft 2011] • Weighted Linear Combination (WLC) [Huang et al. 2011]
Criteria • Normalized Discounted Cumulative Gain • Mean Average Precision
Normalized Discounted Cumulative Gain • Highly relevant documents are more valuable • The lower the ranked position of the relevant document is, the less valuable it is for the user Normalized parameter Gradually reduces the obtained from ideal document score ranking
Normalized Discounted Cumulative Gain Rank Tweet 1 A 2 B 3 C AとFが共にリツイートさ れている時、Fが低くラ 4 D ンク付けされている為、 5 E Fにペナルティを付ける 6 F Normalized parameter Gradually reduces the obtained from ideal document score ranking
Mean Average Precision • Average of the precision of top k documents Precision at ith tweet Number of reposted Retweeted or not tweets
Mean Average Precision Rank Tweet 1 A If F is retweeted, 2 B precision increases. If not, precision 3 C decreases 4 D 5 E 6 F Number of reposted Retweeted or not tweets
Up to top ranked 5 Results tweets • Automatic Evaluation • Manual Evaluation
Evaluation of Components • Automatic Evaluation • Manual Evaluation
Conclusion • Relatively improved 18.3% in DCG and 7.8% in MAP over the best baseline • Improved due to using the tweets and their authors • Succeeded to recommend interesting information that lies outside the user’s followers • Future: Include credibility and recency

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Tweet Recommendation with Graph Co-Ranking

  • 1. Tweet Recommendation with Graph Co-Ranking Rui Yan, Mirella Lapata, Xiaoming Li ACL 2012 Reader: 東京大学 相澤研究室 藤沼祥成
  • 2. Motivation • 3 problems related to tweet recommendation – Linkage of following and retweeting – Interest the user – Personalization and diversity
  • 3. Related Work • Collaborative Filtering [Hannon et al. 2010] • Selecting tweets including URLs [Chen et al. 2010] – And so on… • Co-Ranking Framework: Scientific impact and modeling the relationship between authors and their publications [Zhou et al., 2007].
  • 4. What is Proposed in this Paper • Adapting Co-Ranking framework to Tweet recommendation • Including personalization
  • 5. Graphs Tweet-Author Graph Tweet Graph Author Graph
  • 6. Co-Ranking Algorithm • Simultaneously rank tweets and their authors – a tweet is important if it associates to other important tweets – A user is important if the associate to other important users, and they write important tweets
  • 7. Components of Co-Ranking • Popularity (PageRank [Brin and Page 1998]) • Personalization (PersRank) – Modifying PageRank • Diversity (DivRank [Mei et al. 2010]) – Avoid assigning only high scores to closely connected nodes – Popular nodes get popular
  • 8. Popularity: PageRank • (1-μ): stick to the random walk • μ: Jump to any vertex chosen uniformly at random • m: ranking scores of for the vertices in Tweet graph
  • 9. Personalization (1/2) • Used Latent Dirichlet Allocation to construct the matrix D • Dij: Probabilitiy of tweet mi belongs to topic tj • Image of D Tweets 𝐷11 ⋯ 𝐷1𝑛 Topics ⋮ ⋱ ⋮ 𝐷 𝑚1 ⋯ 𝐷 𝑚𝑛
  • 10. Personalization (2/2) • r: ri = the probability for a user to respond to tweet mi • Estimate t: topic interest vector by maximum likelihood
  • 11. Diversity: DivRank • Transition probabilities change over time • Favors popular nodes as time goes by • After z iterations, M is
  • 13. Actual Steps • Step 1 Walk from the author • Step 2 Walk from the tweet Ensuring convergence
  • 14. Co-Ranking Algorithm • Coupling parameter λ • If λ=0, no coupling between Tweet graph and Author graph • In experiment, λ = 0.6
  • 15. Transition Matrix in Author Graph • It is defined as
  • 16. Transition Matrix in Tweet Graph • Tweet Graph is defined as • • mi a term vector is weighted as tf・idf
  • 17. Transition Matrix in Tweet- Author Graph • MU: • UM: • : tweet mi is authored by uj
  • 18. Data Set • 9,449,542 users – Tracing the edges of 23 users’ followers and followees until no new user is added • 3/25/2011 to 5/30/2011 • 364,287,744 tweets
  • 19. Evaluation • Automatically – Golden: A tweet is retweeted or not • Human-based Judgement – 23 users – Whether they will retweet or not – Calculating the mean
  • 20. Baselines • Randomly ranked (Random) • Longer tweets ranked higher (Length) • Many retweets ranked higher (RTnum) • RankSVM algorithm (RSVM) [Duan et al. 2010] • Decision Tree Classifier (DTC) [Uysal and Croft 2011] • Weighted Linear Combination (WLC) [Huang et al. 2011]
  • 21. Criteria • Normalized Discounted Cumulative Gain • Mean Average Precision
  • 22. Normalized Discounted Cumulative Gain • Highly relevant documents are more valuable • The lower the ranked position of the relevant document is, the less valuable it is for the user Normalized parameter Gradually reduces the obtained from ideal document score ranking
  • 23. Normalized Discounted Cumulative Gain Rank Tweet 1 A 2 B 3 C AとFが共にリツイートさ れている時、Fが低くラ 4 D ンク付けされている為、 5 E Fにペナルティを付ける 6 F Normalized parameter Gradually reduces the obtained from ideal document score ranking
  • 24. Mean Average Precision • Average of the precision of top k documents Precision at ith tweet Number of reposted Retweeted or not tweets
  • 25. Mean Average Precision Rank Tweet 1 A If F is retweeted, 2 B precision increases. If not, precision 3 C decreases 4 D 5 E 6 F Number of reposted Retweeted or not tweets
  • 26. Up to top ranked 5 Results tweets • Automatic Evaluation • Manual Evaluation
  • 27. Evaluation of Components • Automatic Evaluation • Manual Evaluation
  • 28. Conclusion • Relatively improved 18.3% in DCG and 7.8% in MAP over the best baseline • Improved due to using the tweets and their authors • Succeeded to recommend interesting information that lies outside the user’s followers • Future: Include credibility and recency