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 Graph Author

Graph

Tweet-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

𝐷11 ⋯ 𝐷1𝑛

⋮ ⋱ ⋮𝐷𝑚1 ⋯ 𝐷𝑚𝑛

To

pic

s

Tweets

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

• Step 2

Walk from the author

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

• 9,4 49,542 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

Gradually reduces the

document score Normalized parameter

obtained from ideal

ranking

Normalized Discounted

Cumulative Gain

Gradually reduces the

document score Normalized parameter

obtained from ideal

ranking

Rank Tweet

1 A

2 B

3 C

4 D

5 E

6 F

AとFが共にリツイートされている時、Fが低くランク付けされている為、Fにペナルティを付ける

Mean Average Precision

• Average of the precision of top k

documents

Number of reposted

tweets

Precision at ith tweet

Retweeted or not

Mean Average Precision

Number of reposted

tweets Retweeted or not

Rank Tweet

1 A

2 B

3 C

4 D

5 E

6 F

If F is retweeted,

precision increases.

If not, precision

decreases

Results

• Automatic

Evaluation

• Manual

Evaluation

Up to top ranked 5

tweets

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