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Guangyuan Piao, John G. Breslin
Unit for Social Semantics

28th ACM Conference on Hypertext and Social Medial
Prague, Czec...
2
1/3 users seek medical information
and over 50% users consume news
on Social Networks
Facebook and Twitter together gene...
According to a research done by Twocharts,
44% of Twitter users have never sent a tweet
[SOURCE] http://guardianlv.com/201...
How can we infer user interests for passive
users based on the info. of their followees?
! user modeling for active users
•  analyzing users’ tweets
•  representing user interests using different approaches
•  b...
! user modeling for passive users
•  analyzing information of users’ followees
•  HIW(followees_tweet) [Chen et al. SIGCHI...
! user modeling for passive users
•  analyzing information of users’ followees
•  IP(followees_bio) [Piao et al. ECIR’17]
...
Different Views of Followees
! user modeling for passive users
8
Bob	Horry
@bob
Android	developer,	
educator
biographies
(...
Aim of Work
! user modeling for passive users
•  we aim to investigate
•  whether we can leverage the list memberships
of ...
10
Our Approach
! user modeling leveraging list memberships of followees
1	
fetch	user’s	
followees	
3	
extract	en33es	fro...
11
Constructing Primary Interests
! Weighting Scheme 1 (WS1)
•  profile of a followee f in Fu :	
where
•  weight of an ent...
12
Constructing Primary Interests
! Weighting Scheme 2 (WS2)
•  based on the idea of HIW (Chen et al. CHI’10)
•  excluding...
13
Interest Propagation
! interest propagation using DBpedia (SEMANTiCS’16)
•  SP: # of subpages
•  SC: # of subcategories...
14
Interest Propagation
! interest propagation using DBpedia (SEMANTiCS’16)
•  same as previous approach but with DBpedia ...
! main goal
•  analyze & compare different user modeling strategies in the
context of link (URL) recommendations
! link (U...
16
Experiment Setup
! Twitter dataset
•  439 random users
•  2,771 followees on average
•  considered up to 200 followees ...
17
Experiment Setup
! evaluation metrics
•  MRR (Mean Reciprocal Rank)
•  the 1st relevant item occurs on average in recom...
18
Info. of List Memberships
•  over 90% users, at least 1 list membership
•  173 list memberships, on average
•  3,047 vs...
Results
•  some significant improvement
when # of followees is small
0	 10000	 20000	 30000	 40000	 50000	
50	
100	
150	
200	
#	of	en''es	
#	of	followees	
without	refinement	 with	refinement	
R...
Results – combining two views
•  combining two views of followees
The final rank of an item is determined by
the average r...
Results – combining two views
•  combining two views of followees
0.0400	
0.0450	
0.0500	
0.0550	
0.0600	
0.0650	
0.0700	
...
Conclusions
•  leveraging list memberships of followees > exploiting biographies
especially in the case of a user having a...
24
Thank you for your attention!
Guangyuan Piao
homepage: http://parklize.github.io
e-mail: guangyuan.piao@insight-centre....
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Hypertext2017-Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter

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Presentation at 28th ACM Conference on Hypertext and Social Media

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Hypertext2017-Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter

  1. 1. Guangyuan Piao, John G. Breslin Unit for Social Semantics 28th ACM Conference on Hypertext and Social Medial Prague, Czech Republic, 4-7, July, 2017 Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter
  2. 2. 2 1/3 users seek medical information and over 50% users consume news on Social Networks Facebook and Twitter together generate more than 5 billion microblogs / day [SOURCE] Semantic Filtering for Social Data, Amit et al., Internet Computing’16
  3. 3. According to a research done by Twocharts, 44% of Twitter users have never sent a tweet [SOURCE] http://guardianlv.com/2014/04/twitter-users-are-not-tweeting/
  4. 4. How can we infer user interests for passive users based on the info. of their followees?
  5. 5. ! user modeling for active users •  analyzing users’ tweets •  representing user interests using different approaches •  bag-of-words •  topic modeling •  bag-of-concepts dbr:Eagles_of_Death_Metal (5) Related Work 5 interest frequency dbr:The_Wombats (2) dbc:Hard_rock dbp:genre
  6. 6. ! user modeling for passive users •  analyzing information of users’ followees •  HIW(followees_tweet) [Chen et al. SIGCHI’10] •  a great amount of data, but also noisy •  SA(followees_name) [Besel et al. SAC’16, Faralli et al. SNAM’16] •  link names to entities, construct category-based user profiles •  spreading activation + WiBi-taxonomy (Wikipedia categories) Related Work 6 dbr:Cristiano_Ronaldo (5) dbc:Real_Madrid_C.F._players dbr:2014_FIFA_World_Cup_players Category A Category B … …
  7. 7. ! user modeling for passive users •  analyzing information of users’ followees •  IP(followees_bio) [Piao et al. ECIR’17] •  exploring related categories & entities (1-hop) •  performed better than HIW(followees_tweet) SA(followees_name) Related Work 7 Bob Horry @bob Android developer, educator dbr:Android_(operating_system) dbc:Smartphones dbr:Java_(programming_language) dbc:Tablet_operating_systems dc:subject dc:subjectdbp:programmedIn
  8. 8. Different Views of Followees ! user modeling for passive users 8 Bob Horry @bob Android developer, educator biographies (self-description) list memberships (others-descriptions)
  9. 9. Aim of Work ! user modeling for passive users •  we aim to investigate •  whether we can leverage the list memberships of followees for inferring user interest profiles, •  whether two different views of followees complement each other to improve the quality of user profiles 9
  10. 10. 10 Our Approach ! user modeling leveraging list memberships of followees 1 fetch user’s followees 3 extract en33es from followees’ list memberships 5 interest propaga3on Twitter user @alice Interest profile Twitter API Tag.me DBpedia graph 2 fetch list memberships of followees 4 construc3ng primary interests Twitter API weigh3ng scheme
  11. 11. 11 Constructing Primary Interests ! Weighting Scheme 1 (WS1) •  profile of a followee f in Fu : where •  weight of an entity with respect to the target user A (0.1) B (0.2)F (0.1) … … … B (0.3) F (0.2)C (0.2) … normalized followee profile Fu B (0.5) … F (0.3) …
  12. 12. 12 Constructing Primary Interests ! Weighting Scheme 2 (WS2) •  based on the idea of HIW (Chen et al. CHI’10) •  excluding entities extracted only in a single followee •  w(u, cj) = the number of followees who have cj in their list memberships. A BF … … … B FC … B (2) … F (2) …
  13. 13. 13 Interest Propagation ! interest propagation using DBpedia (SEMANTiCS’16) •  SP: # of subpages •  SC: # of subcategories •  P: # of properties appearing in the whole DBpedia graph •  intuition 1: discount common categories •  intuition 2: discount related entities connected with common properties dbr:Android_(operating_system) dbc:Smartphones dbr:Java_(programming_language) dbc:Tablet_operating_systems dc:subject dc:subjectdbp:programmedIn
  14. 14. 14 Interest Propagation ! interest propagation using DBpedia (SEMANTiCS’16) •  same as previous approach but with DBpedia refinement •  extracting sub-graph of dbc:Main_topic_classifications •  merging categories and entities with the same names dbc:Apple_Inc. (0.25) dbr:Apple_Inc. (5) dbr:Steve_Jobs (2) Apple_Inc. (5.25) Steve_Jobs (2) before after
  15. 15. ! main goal •  analyze & compare different user modeling strategies in the context of link (URL) recommendations ! link (URL) profile •  same representation model for users, based on its content ! ground truth •  links shared by users in their timeline in the last two weeks 15 Experiment Setup UM#1 UM#2 candidate links (URLs) recommendation algorithm (cosine similarity) top-N recommendations
  16. 16. 16 Experiment Setup ! Twitter dataset •  439 random users •  2,771 followees on average •  considered up to 200 followees for each user due to the Twitter API limit for crawling list memberships ! dataset for experiment •  439 users •  74,488 followees in total, 170 followees on average •  15,053 candidate links for recommendations
  17. 17. 17 Experiment Setup ! evaluation metrics •  MRR (Mean Reciprocal Rank) •  the 1st relevant item occurs on average in recommendations •  S@N (Success rate) •  mean probability of a relevant item occurs in the top-N list •  P@N (Precision) •  mean probability of retrieved items in the top-N are relevant •  R@N (Recall) •  mean probability of relevant items retrieved in in the top-N
  18. 18. 18 Info. of List Memberships •  over 90% users, at least 1 list membership •  173 list memberships, on average •  3,047 vs. 23 entities from list memberships vs. bios considering up to 50 followees
  19. 19. Results •  some significant improvement when # of followees is small
  20. 20. 0 10000 20000 30000 40000 50000 50 100 150 200 # of en''es # of followees without refinement with refinement Results 9% compression of profile size, while remaining at a similar performance level
  21. 21. Results – combining two views •  combining two views of followees The final rank of an item is determined by the average rank position of each rank based on two user models (Ryen et al. SIGIR’09) score = x : rank position based on 1st user model y : rank position based on 2nd user model β : importance control parameter •  combining two views improves the performance significantly
  22. 22. Results – combining two views •  combining two views of followees 0.0400 0.0450 0.0500 0.0550 0.0600 0.0650 0.0700 0.0750 0.0800 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 R@10 beta 50 100 150 200 best performance when β = 0.1, similar results for MRR, P@10, S@10 •  list memberships paly more important role in the combination
  23. 23. Conclusions •  leveraging list memberships of followees > exploiting biographies especially in the case of a user having a small number of followees •  combining the two different views of followees can improve the quality of user modeling significantly, •  and the list memberships of followees play a more important role in the combination
  24. 24. 24 Thank you for your attention! Guangyuan Piao homepage: http://parklize.github.io e-mail: guangyuan.piao@insight-centre.org twitter: https://twitter.com/parklize slideshare: http://www.slideshare.net/parklize

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