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

Agenda 4 Introduction The Proposed Approaches Experiments Conclusion Literature Review

1 Introduction

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

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

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

Tag Cloud

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

Literature Review 2

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

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

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

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)

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

The Proposed Approaches 3

 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

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

 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

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” Number of users used the tag for the item Number of users collected the item

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”

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

 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”

 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

 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”

 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”

 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”

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

 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

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

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

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

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

Experiments 4

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

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

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

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

 Comparison results of Dataset D2 Results-I

 Parameter setting (Dataset D1)  TM-User:  : ,  1 :  TM-Item:  1 : Results-2 Taxonomy Model

 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

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)

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)

Conclusions 5

 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

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

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

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

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