1. Postdoc, School of Information, University of Arizona
Profiling users with tag networks in diffusion-based personalized recommendation
Jin Mao
Postdoc, School of Information, University of Arizona
Oct. 30, 2015

2. Outline
Introduction
Tag-based User Profiling
Diffusion-based Personalized Recommendation
Experiments
Discussion

3. Introduction
Personalized Information Recommendation
Recommender System (Resnick and Varian, 1997): a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item.

4. Introduction
Social Tagging System
Social tagging system consists of users, resources, social tags and their associations behind the tagging behaviour(Hotho et al., 2006)

5. Implicit rating or voting behaviour against items (Liang et al., 2008)
Introduction
Tags
Personal collections
Implicit rating or voting behaviour against items (Liang et al., 2008)

6. Tag-based User Profiling
Personalization
User profiling aims to understand personal interests of users and describe the interests with some kind of representation(Liang, 2010).
Explicit: demographic information
Implicit: click streams, customer purchase logs
content
behaviour
Tag

7. Tag-based User Profiling
Methods
The common way to construct a tag-based user profile is to obtain a representation of a tag vector or a set of tags.
Selecting suitable candidate tags and inferring their weights are the major focus.
Vector Space Model (Vallet et al., 2009; Bogers and Bosch, 2009)
Tag Set(Yeung et al., 2008)
Connect personal tag vocabularies to common folksonomy (Wetzker et al., 2010), or canonical ontologies(Movahedian and Khayyambashi, 2014; Han et al, 2010; Hsu 2012)

8. Tag-based User Profiling
Co-occurrence approach
It is more reasonable to profile users’ interests with tags in combination rather than independently (Michlmayr and Cayzer 2007).
Weighted tag network
How to infer the weight of tags?

9. Diffusion-based Personalized Recommendation
Main Steps
Construct the user tag network
Infer the weights of tags
Generate the scores of recommended items via a diffusion process

10. Diffusion-based Personalized Recommendation
User tag network
Tripartite Graph

11. Diffusion-based Personalized Recommendation
User tag network

12. Diffusion-based Personalized Recommendation
Inferring tag weights

13. Diffusion-based Personalized Recommendation
Inferring tag weights

14. Diffusion-based Personalized Recommendation
Diffusion process
Tag-item bipartite graph
An edge represents the usage of the tag to annotate the item in the collection
The weights denote the frequency of the usage.
The tag-item bipartite graph manifests the opinion of other users in the system.

15. Diffusion-based Personalized Recommendation
Diffusion process

16. Avg Tag Assignments per User
Experiments
Dataset
The HetRec 2011 conference
Collection
Users
Items
# of unique Tags
Tag Assignments
Avg Tag Assignments per User
Std.dev
Last.fm
1,892
12,523
9,749
186,725
98.56
224.53
MovieLens
2,113
5,908
9,079
47,957
22.70
169.95
Delicious
1,867
69,223
40,897
437,593
234.38
192.39

17. Statistics about the user tag networks of the three datasets
Experiments
Dataset
Statistics about the user tag networks of the three datasets
Collection
Min Nodes
Max Nodes
Median Nodes
Avg Edges
Avg Density
Last.fm 1 50 12
101.74
0.399
MovieLens
1660 2
21.30
0.301
Delicious
739
105
550.35
0.087
The user tag networks in Last.fm and MovieLens are denser than Delicious

18. Inter-diversity (Zhou et al., 2008) ---measure the personalization
Experiments
Evaluation Metrics
Recall
Inter-diversity (Zhou et al., 2008) ---measure the personalization
Baseline model
The tag-based collaborative filtering (Tso-Sutter et al. 2008)

19. The performance of different tag-aware recommendation methods
Experiments
Results
The performance of different tag-aware recommendation methods
Table 3. The performance of the diffusion-based methods and the tag-based collaborative filtering (TBCF: tag-based collaborative filtering; TNDR_PR: the diffusion-based method using PageRank node weighting approach; TNDR_HITS: the diffusion-based method using HITS node weighting approach. )
Collection
Metric
TBCF
TNDR_PR
TNDR_HITS
Last.fm
3.020%
3.126%†
3.182%†
2.387%
2.394%†
2.421%†
Recall
1.648%
1.667%†
IntDiv
83.820%
93.046%†
92.149%†
MovieLens
1.322%
1.930%†*
1.629%†
0.979%
1.429%†*
1.701%
2.424%†*
88.030%
97.831%†
97.783%†
Delicious
0.579%
0.277%
0.202%
0.428%
0.195%
0.287%
0.131%
0.135%
97.100%
99.729%†
99.591%†
Figures with † denote improvements over the baseline model. Stars indicate statistically significant improvements over the baseline model according to the Wilcoxon test at the level of 0.05.
Precision, recall and personalization are improved.

20. The influence of the refinement process
Experiments
Results
The influence of the refinement process
Table 4. The performance of different diffusion-based methods(TNDR_ILW: the diffusion-based method in which the TF-IUF approach is used to initialize tag weights; TNDR_1: the diffusion-based method in which tag weights are initialized as 1. )
Collections
Metrics
TNDR_ILW
TNDR_ILW_PR
TNDR_ILW_HITS
TNDR_1
TNDR_1_PR
TNDR_1_HITS
Last.fm
2.861%
3.126%†*
3.182%†*
2.903%
3.224%†*
3.168%†
2.268%
2.394%†*
2.421%†
2.331%
2.394%†
2.435%† R
1.562%
1.648%†
1.667%†
1.605%
1.677%†
IntDiv
94.472%
93.046%
92.149%
92.397%
91.825%
91.639%
MovieLens
1.830%
1.930%†
1.629%
1.529%
1.729%†
1.579%†
1.391%
1.429%†
1.203%
1.291%†
1.316%†
2.360%
2.424%†
2.041%
2.190%†
2.233%†
97.849%
97.831%
97.783%
97.738%
97.746%†
97.720%
Delicious
0.264%
0.277%†
0.202%
0.252%†
0.195%
0.145%
0.170%†
0.195%†
0.135%
0.131%
0.097%
0.114%†
0.131%†
99.750%
99.729%
99.591%
99.666%
99.628%
99.525%
Figures with † denote improvements over the baseline model. Stars indicate statistically significant improvements over the baseline model according to the Wilcoxon test at the level of 0.05.
The refinement methods can be regarded as a necessary process for the proposed diffusion-based methods.

21. The influence of the damping factor
Experiments
Results
The influence of the damping factor
A proper value for the damping factor is dependent on the collection.
In order to recommend diverse items, only the initial tag weights should be applied.

22. The relationships among tags
Discussion
Implication
The relationships among tags
Apply the network theory in personalized recommendation
Practical guidance:
Effectiveness
A real-time dynamic
Different recommendation strategies

23. Different node weighting methods Larger datasets
Discussion
Limitation
Our method achieves improved performance in datasets with denser user tag networks  generalization: in-depth case analysis of user examples
Different node weighting methods
Larger datasets

24. Further explore the relationships of tags in network theory
Discussion
Future study
Further explore the relationships of tags in network theory
More statistical properties (such as degree centrality, betweeness centrality and community structure) of user tag networks

25. ACKNOWLEDGEMENT
Mao, J., Lu, K., Li, G., & Yi, M. (2015). Profiling users with tag networks in diffusion-based personalized recommendation. Journal of Information Science,
We thank the anonymous reviewers for their comments that have contributed to important improvements of the paper.
We also thank Professor Dietmar Wolfram for his help.

26. Thank you!