A Recommender System based on Tag and
Time Information for Social Tagging Systems
Nan Zheng and Qiudan Li (Chinese Academy of Sciences)
Expert Systems with Applications, 2011
February 16, 2011
Hyunwoo Kim
Outline
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Introduction
Proposed Approach
A Recommender System
Experimental Evaluation
Conclusion
Introduction
Tag
Time
- Interests of a user
- Bridge between a user and a resource
- First posting date
- The latest posting date
- Posting frequency
3
Introduction
Time??
- Page creation date
Web Page
4
Introduction
 Tags
– Reflects the interest of a user as time goes by
 For example,
– Alice often uses baby health and education her bookmarks
Alice
baby health
in 2006
education
in 2011
5
Introduction
 A resource-recommendation model
– Providing personalized services in social tagging systems
– By three phases
 Rating generation
 User similarity calculation
 Resource recommendation
 Three strategies to generate ratings
– Tag-weight strategy
– Time-weight strategy
– Tag and time strategy
6
Proposed Approach
 Two matrices
– User-resource binary matrix
 If a user has bookmarked a resource, the value is 1
 Otherwise 0
User
– Modified user-resource rating matrix
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Resource
1
0
0
1
1
0
0
1
1
Involving either tags, time, or both tag and time
Tag-weight: tag frequency of a user
Time-weight: time weight value
Tag and time: the value of the integration of tag weight value and time weight
value
Resource
User
7
0.5
0
0
0.3
0.7
0
0
0.1
0.9
Proposed Approach
 The framework of resource-recommendation model
8
Proposed Approach
- Rating Generation
 Tag-weight strategy
– Assumption
 The more a tag has been used, the more interests the user has in the related
resource
 A user is likely to prefer the resources bookmarked with the high frequency
tags
– Tag weight is defined as
tag(u,r): the set of tags with which a user u has bookmarked to a resource r
wu,ta: tag score of each tag ta in tag(u,r)
9
Proposed Approach
- Rating Generation
 Time-weight strategy
– Assumption
 Human interests drift as time goes by
– To learn and track the changes of user’s behavior
 Time window
 Exponential forgetting function
– Time weight defined as
time(u,r): non-negative integer. The value of 0 for the last tagging day and the
value of 1 for the penultimate tagging day, and so on
hlu: half-life for each user
10
Proposed Approach
- Rating Generation
 Tag and time strategy
– Combining two weights into a single one
– Tag and time weight defined as
 Linear combination of tag weight and time weight
– In order to denote user’s preference more accurately
 Tags indicate user’s degree of preferences
 Bookmarked time reflects interest drifts of a user
11
Proposed Approach
 An example
12
A Recommender System
 The architecture of the recommender system
History browsing: tagging history browsing and tag browsing
User network construction: network is constructed according to user similarity
Resource recommendation: proposed model and log-based model
13
A Recommender System
 Tag browsing
14
A Recommender System
 Log-based model
15
A Recommender System
 Proposed resource-recommendation model
16
Experimental Evaluation
 Evaluation metrics
– Hit-rate and hit-rank
m: the total number of users
h: the number of hits
pi: positions
17
Experimental Evaluation
 Tag’s impact
18
Experimental Evaluation
 Time’s impact
19
Experimental Evaluation
 Model with both tag and time information
20
Conclusion
 In this paper
– Proposing a resource-recommendation model to utilize tag and time
information
 Tag, time and both tag and time outperform traditional log-based model
– Building a recommender system to provide personalized resource
recommendation
 Future work
– Evaluating proposed resource-recommendation model with other datasets
– Extending to social network analysis in social tagging systems
21
Thank You