1. User Profiling based on Folksonomy Information in Web 2.0 for Personalized Recommender Systems Huizhi (Elly) Liang Supervisors: Yue Xu, Yuefeng Li, Richi Nayak Queensland University of Technology, Australia

2. Agenda 4 Introduction 1 2 3 5 The Proposed Approaches Experiments Conclusion Literature Review

3. 1 Introduction

4. Information overload  Personalization “Personalization is the ability providing content and services tailored to individuals based on knowledge about their preferences and behaviours” (Hagen, 1999)  Recommender systems  User profiling  Explicit user profiles Explicit ratings  Implicit user profiling Web log Other information sources

5. Web 2.0  Web 2.0: Read and Write web (O’Reilly Media, 2004)  A platform for users to conduct online participation, collaboration and interaction.  Expressing opinions, sharing information, building networks  Wikipedia, Facebook, Delicious, Tweeter  Plenty of new user information  Folksonomy (Tags), reviews, networks, blogs, micro-blogs etc.  Opportunities  Providing possible new solutions to profile users

6. Folksonomy  Folksonomy= folk + taxonomy  Tags: Typical Web 2.0 information  Keywords given by users to organize and classify items  The wisdom of crowds  Multiple functions Item organizing and sharing Building networks Expressing users’ explicit topic interests and opinions

7. Tag Cloud

8. Folksonomy Tags Taxonomy categories  Taxonomy  Given by experts  Standard vocabulary & Structural relationship  Well recognized as common knowledge  Independent with user communities  No users’ personal viewpoints or preferences information  Taxonomy  Given by experts  Standard vocabulary & Structural relationship  Well recognized as common knowledge  Independent with user communities  No users’ personal viewpoints or preferences information  Folksonomy  Given by users explicitly and proactively  Reflecting users’ personal viewpoints and topic preferences  Less intrusive & Multiple function  Lightweight textural information  Contains a lot of noise  Folksonomy  Given by users explicitly and proactively  Reflecting users’ personal viewpoints and topic preferences  Less intrusive & Multiple function  Lightweight textural information  Contains a lot of noise

9. Literature Review 2

10. User Profiling  Web User profiling  Web content & structure  Web log & Web usage  Taxonomy & Ontology  User Profiling in Web 2.0  New user information sources Folksonomy, blogs, reviews, micro-blogs Videos, audios, images Friends, trust network, followers, following

11. User Profiling 2  User Profiling based on folksonomy  Approaches Users’ own tags Associated tags Latent topics of tags Popular tags  Challenges Distinctive features of tags Tag quality problem Semantic ambiguity and synonyms About 60% of tags are personal tags

12. Recommender system  Recommendation tasks  Top N Recommendation (Precision, Recall, F1)  Rating Prediction (Mean Absolute Error, Root Mean Squared Error)  Recommendation approaches  Content based Term vector model Latent Dirichlet Allocation (LDA)  Collaborative Filtering (CF) Memory based CF: User-KNN & Item-KNN Model based CF: Matrix Factorization techniques  Hybrid

13. Recommender system 2  Recommender systems based on Taxonomy  Ziegler’s approach (CIKM, 2004)  Recommender systems based on Folksonomy  Tag recommendations Tensor based approach (KDD, 2009) Graph based approach (SIGIR, 2009)  Item recommendations Tso-Sutter’s approach(SAC, 2008) Clustering (RecSys, 2009) LDA approach (HT, 2009) Graph Rank (2010) Special tag rating function(WWW,2009)

14. Research Problem  Research Gap  Features of folksonomy  Noise of folksonomy  Combining with taxonomy  Research Problem  Profiling users based on folksonomy information in Web 2.0 and enhance recommender systems

15. The Proposed Approaches 3

16.  User Profiling Models  User Profiling based on Folksonomy  User Profiling based on Taxonomy  Hybrid User Profiling  Recommender System  Top N item recommendation Recommendation making The Proposed Approaches User Profiling User Profiling-Folksonomy User Profiling-Taxonomy User Profiling-Hybrid

17. The Relationship Modelling  The Multiple relationships of tagging  Two dimensional relationships User-Item relationship User-Tag relationship Item-Tag relationship  Three dimensional relationship Personal tagging behavior User-Tag-Item relationship (User×Tag)-Item mapping Item-(User×Tag) mapping

18.  Part 1: User Profiling Approaches based on Folksonomy  Tag representation-Folksonomy  Item representation-Folksonomy  User representation-Folksonomy Tag Representation- Folksonomy Item Representation- Folksonomy User Representation- Folksonomy User Profiling-Folksonomy

19. Tag representation-Folksonomy  Reduce the noise of tags  Find the personally related tags of each tag  Determine the relevance weight  Relevance weight of two tags with respect to a user  The collected items of a tag  The expectation of the probability of a tag being used for the collected items “apple” “garden” “globalization” “apple” “internet” 0.16 0.34 Number of users used the tag for the item Number of users collected the item

20. Item representation-Folksonomy  Expand the tags of each item  Find the relevant tags of each item  Determine the relevance weight  The relevance of an item to a tag  User-tag pairs  The relevance of two tags with respect to a user  Inverse item frequency “garden” “apple” “globalization” “internet” “0403”

21. User Representation-Folksonomy  Find users’ preferences to tags  The preference weight of a user to a tag  Preferences to one tag  The relevance of two tags with respect to a user  Inverse user frequency “garden” “apple” “globalization” “internet” “0403” Number of items collected with the tag by the user Number of items collected by the user

22.  User  Item preferences Implicit ratings  Topic preferences Tag vocabulary  Item  Tag vocabulary User Profiling-Folksonomy “garden” “apple” “globalization” “internet” “0403” “garden” “apple” “globalization” “internet” “0403”

23.  Part 2: User Profiling based on Taxonomy  Advantages of Taxonomy Standard vocabulary Well recognized Independent with user communities Experts’ viewpoints  Representations Item representation-Taxonomy Tag representation-Taxonomy User representation-Taxonomy “apple” Tag Representation- Taxonomy Item Representation- Taxonomy User Representation- Taxonomy User Profiling-Taxonomy

24.  Find the relevant taxonomic topics of each item  The relevance of an item to a taxonomic topic  The average weight of a taxonomic topic in all descriptors The weight of a taxonomic topic in an item descriptor Deploy weight from leaf topic to root topic  Inverse item frequency Item Representation-Taxonomy “programming” “book” “computers” “networks”

25.  Reduce the noise of tags  Find the personal semantic meaning of each tag  The relevance of a tag to a taxonomic topic with respect to a user  The collected items of a tag  Average relevance weight of a taxonomic topic to the collected items Tag Representation-Taxonomy “computers” “programming” “databases” “networks” “apple” “garden” “flowers” “fruit” “apple”

26.  Find users’ preferences to taxonomic topics  The preference weight of a user to a taxonomic topic  Preference to a tag  Relevance of a tag to a taxonomic topic with respect to the user  Inverse user frequency User Representation-Taxonomy “databases” “programming” “computers” “book” “0403”

27.  User  Item preferences Implicit ratings  Topic preferences Taxonomy vocabulary  Item  Taxonomy vocabulary User Profiling-Taxonomy “databases” “programming” “computers” “book” “computers” “programming” “networks”

28.  Part 3: Hybrid User Profiling  Combine Part 1 and Part 2  Wisdom of crowds Tag vocabulary & Users’ viewpoints  Wisdom of experts Taxonomy vocabulary & Experts’ viewpoints Tag representation-Hybrid Item representation-HybridUser representation-Hybrid

29.  Personalized Recommendation Making  Top N item recommendation Neighborhood Formation Recommendation Generation User Profiling-Folksonomy User Profiling-Taxonomy User Profiling-Hybrid User Profiling Recommendation Making

30. Neighbourhood Formation  K-Nearest Neighbourhood  User-KNN Similarity of item preferences Similarity of topic preference Tags Taxonomic topics Linear combination Item Preferences Topic Preferences Tags Taxonomic topics User Similarity

31. Neighbourhood Formation 2  K-Nearest Neighbourhood  Item-KNN Similarity of Tags Similarity of Taxonomic topics Linear combination TagsTaxonomic TopicsItem similarity

32. Recommendation Generation  Candidate items  Neighbour items & Not tagged by the target user  User based recommendation  Item based recommendation Prediction ScoreUser Similarity Content matching Tags Taxonomic Topics Prediction Score Item Similarity

33. Experiments 4

34. Datasets  D1: Amazon.com  4112 users, 34201 tags, 30467 items, 9919 taxonomic topics  D2: CiteULike “Who-posted-what” dataset  7103 users, 78414 tags, 117279 items  Power Law Distributions Tags Items

35. Experiment setup  Top N item recommendation  Experiment setup 5-folded 80% training & 20% testing  Evaluation Metrics Precision, Recall, F1 Measure  Comparisons  Proposed Models Folksonomy Model: FM-User, FM-Item Taxonomy Model: TM-User, TM-Item Hybrid Model: FTM-User, FTM-Item  Baseline Models

36.  Tag Noise Removing Approaches (Dataset D1)  Parameter setting  FM-User: : 0.8-1.0,  1 : 0.4-0.5  FM-Item:  1 : 0.4-0.5 Results-I Folksonomy Model

37.  The Comparison of the State-of-the-art approaches (Dataset D1) Results-I

38.  Comparison results of Dataset D2 Results-I

39.  Parameter setting (Dataset D1)  TM-User:  : 0.8-1.0,  1 : 0.4-0.5  TM-Item:  1 : 0.4-0.5 Results-2 Taxonomy Model

40.  Parameter setting (Dataset D1)  FTM-User:  FTM-Item:  1 =0.3,  Hybrid Models v.s. Single Models  Folksonomy Model v.s. Taxonomy Model Results-3 Hybrid Models

41. Results-3  The influence of personal tags  D1 personal tags: 67%,   10: 4.8%  D2 personal tags: 70%,   10: 5.2%  Findings  Personal tags can improve the precision results  Precision values decreased dramatically when large number (i.e., 90%) of tags (i.e.,  5) was removed. TM-User, D1 (9919, 0.24)

42. Discussions  The proposed approaches outperformed other related work  The Hybrid Model performed the best  Each tag counts  Folksonomy can be used as quality information source (rich personalization information)

43. Conclusions 5

44.  Web 2.0  New user information  Modelling the relationships of tagging behaviour  Tag quality problem  The wisdom of crowds & experts  Proposed three user profiling models  User profiling based on folksonomy  User profiling based on taxonomy  Hybrid user profiling  Utilized the proposed user profiles to improve recommender systems  User based  Item based  Evaluation Experiments

45. Contributions  Advantages  Domain free  Language free  Information overload  User profiling and web personalization  Recommender systems  Web 2.0

46. Future Work  Time factor  Cross folksonomy recommendations  Mobile platform application  Integrate with other user information  Explicit ratings  Tweets  Friendship network

47. Published Work  Liang, H. et al. (2010). Personalized Recommender System Based on Item Taxonomy and Folksonomy. CIKM  Liang, H. et al. (2010). Connecting Users and Items with Weighted Tags for Personalized Item Recommendations. Hypertext  Liang, H. et al. (2010). A Hybrid Recommender System based on Weighted Tags. SDM Workshop  Liang, H. et al. (2010). Mining Users’ Opinions based on Item Folksonomy and Taxonomy for Personalized Recommender Systems. ICDM Workshop  Liang, H. et al. (2010). Parallel User profiling based on folksonomy for Large Scaled Recommender Systems- An implementation of Cascading MapReduce. ICDM Workshop  Liang, H. et al. (2009). Collaborative Filtering Recommender Systems based on Popular Tags. ADCS  Liang, H. et al. (2009). Tag Based Collaborative Filtering for Recommender Systems. RSKT  Liang, H. et al. (2009). Personalized Recommender Systems Integrating Social tags and Item Taxonomy. WI  Liang, H. et al. (2008). Collaborative Filtering Recommender Systems Using Tag Information. WI Workshop  Bhuiyan, T., Xu, Y., Jøsang, A., & Liang, H. (2010). Developing Trust Networks Based on User Tagging Information for Recommendation Making. WISE

48. Acknowledgements Time Supervisor Team HPC group Penal Members ISS Anonymous Reviewers Papers Staffs Colleagues Friends Google Books Sunshine CSC Trees Stars Music Trips Blogs Beaches Family …

49. Questions & Answers oklianghuizi@gmail.com