INHA UNIVERSITY INHA UNIVERSITY INCHEON, KOREA INCHEON, KOREA http:// eslab.inha.ac.kr/ Collaborative Tagging in Collaborative Tagging in Recommender Systems Recommender Systems AE-TTIE JI AE-TTIE JI 1 , CHEOL YEON , CHEOL YEON 1 , HEUNG-NAM KIM , HEUNG-NAM KIM 1 , AND GEUN-SIK JO , AND GEUN-SIK JO 2 1 Intelligent E-Commerce Systems Laboratory, Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information Engineering, Inha University Department of Computer Science & Information Engineering, Inha University {aerry13 , , entireboy , , nami }@eslab.inha.ac.kr }@eslab.inha.ac.kr 2 School of Computer Science & Engineering, Inha University, School of Computer Science & Engineering, Inha University, 253 Yonghyun-dong, Incheon, Korea 402-751 253 Yonghyun-dong, Incheon, Korea 402-751 [email protected]
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INHA UNIVERSITY INCHEON, KOREA Collaborative Tagging in Recommender Systems AE-TTIE JI 1, CHEOL YEON 1, HEUNG-NAM KIM 1, AND GEUN-SIK.
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INHA UNIVERSITYINHA UNIVERSITYINCHEON, KOREAINCHEON, KOREA
http://eslab.inha.ac.kr/
Collaborative Tagging in Collaborative Tagging in Recommender SystemsRecommender Systems
AE-TTIE JIAE-TTIE JI11, CHEOL YEON, CHEOL YEON11, HEUNG-NAM KIM, HEUNG-NAM KIM11, AND GEUN-SIK JO, AND GEUN-SIK JO22
11 Intelligent E-Commerce Systems Laboratory, Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information Engineering, Inha University Department of Computer Science & Information Engineering, Inha University
22 School of Computer Science & Engineering, Inha University, School of Computer Science & Engineering, Inha University, 253 Yonghyun-dong, Incheon, Korea 402-751 253 Yonghyun-dong, Incheon, Korea 402-751
- 18 -INHA UNIVERSITYINHA UNIVERSITYINCHEON, KOREAINCHEON, KOREA
http://eslab.inha.ac.kr/
Benchmark AlgorithmsBenchmark Algorithms User-based Collaborative Filtering User-based Collaborative Filtering (Badrul Sarwar, and et al., 2000)
Item-based Collaborative Filtering Item-based Collaborative Filtering (Mukund Deshpande, and et al., 2004)
KNNKNN size was set to 50 where the performance increase size was set to 50 where the performance increase rates were diminished for main comparison.rates were diminished for main comparison.
Experimental ResultsExperimental Results
5.0%
5.5%
6.0%
6.5%
7.0%
7.5%
8.0%
8.5%
9.0%
9.5%
10 30 50 70 100
Neighborhood Size (k )
reca
ll
User-based CF
Item-based CF
Recommendation size N = 10
- 19 -INHA UNIVERSITYINHA UNIVERSITYINCHEON, KOREAINCHEON, KOREA
http://eslab.inha.ac.kr/
Experiments with CTS sizeExperiments with CTS size
The size of The size of CTSCTS, , ww, can be a significant factor affecting the , can be a significant factor affecting the quality of recommendation.quality of recommendation.
w w was set to 70, which obtained the best quality for main was set to 70, which obtained the best quality for main comparisons.comparisons.
Experimental ResultsExperimental Results
6.0%
6.5%
7.0%
7.5%
8.0%
8.5%
9.0%
10 30 50 70 100
Candidate Tag Set Size (w)
reca
ll
Tag-based CF
Neighbor size k = 50Recommendation size N = 10
- 20 -INHA UNIVERSITYINHA UNIVERSITYINCHEON, KOREAINCHEON, KOREA
http://eslab.inha.ac.kr/
Comparisons of Overall PerformanceComparisons of Overall Performance
Sparsity of the collected dataset affected the performances Sparsity of the collected dataset affected the performances of all three methods.of all three methods.
Even though the number of recommended items were Even though the number of recommended items were small, our method outperformed the other two methods.small, our method outperformed the other two methods.
Experimental ResultsExperimental Results
6.0%
7.0%
8.0%
9.0%
10.0%
11.0%
12.0%
10 20 30 40 50
A number of Recommended Items (N )
reca
ll
Tag-based CFItem-based CFUser-based CF
Neighbor size k = 50CTS size w = 70
- 21 -INHA UNIVERSITYINHA UNIVERSITYINCHEON, KOREAINCHEON, KOREA
http://eslab.inha.ac.kr/
For cold-start users who do not have enough preference For cold-start users who do not have enough preference information, our method outperformed the other two information, our method outperformed the other two methods.methods.
Comparisons for Cold-start UserComparisons for Cold-start User
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
< 3 < 6 < 300Boomarks
reca
llTag-based CF Item-based CF User-based CF
Experimental ResultsExperimental Results
Neighbor size k = 50CTS size w = 70Recommendation size N = 10
- 22 -INHA UNIVERSITYINHA UNIVERSITYINCHEON, KOREAINCHEON, KOREA
http://eslab.inha.ac.kr/
ConclusionConclusion
We analyzed the potential of collaborative tagging We analyzed the potential of collaborative tagging system for applying to recommendation.system for applying to recommendation. User-created tags imply users’ preferences about items as well
as metadata about them. Using tags can partially improve data sparsity and cold-start
user problem which are serious limitations of CF recommendation.
Also proposed is a novel recommender system based Also proposed is a novel recommender system based on collaborative tags of users using CF scheme.on collaborative tags of users using CF scheme. Our algorithm obtained better recommendation quality compared
to traditional CF schemes. It provided more suitable items for user preferences even though
the number of recommended items were small.
- 23 -INHA UNIVERSITYINHA UNIVERSITYINCHEON, KOREAINCHEON, KOREA
http://eslab.inha.ac.kr/
Future WorkFuture Work
““Noise” tags can be included in CTS.Noise” tags can be included in CTS. Some tags are too personalized or content-criticizable (e.g., bad,
myWork, to read etc.)
They should be treated for more personalized and valuable analysis.
There are common issues of keyword-based analysis.There are common issues of keyword-based analysis. Polysemy, synonymy and basic level variation.
Semantic tagging is an interesting approach to address these issues.