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Tag-aware recommender systems by fusion of collaborative filtering algorithms

Published: 16 March 2008 Publication History

Abstract

Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tag-aware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.

References

[1]
G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst., 23(1):103--145, 2005.
[2]
M. Balabanovic and Y. Shoham. Fab: Content-based, collaborative recommendation. Commun. ACM, 40:66--72, 1997.
[3]
J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering. In Proceedings of the twenty-first international conference on Machine learning, pages 65--72, New York, NY, USA, 2004. ACM Press.
[4]
C. Basu, H. Hirsh, and W. Cohen. Recommendation as classification: using social and content-based information in recommendation. In AAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, pages 714--720, Menlo Park, CA, USA, 1998. American Association for Artificial Intelligence.
[5]
D. Benz, K. Tso, and L. Schmidt-Thieme. Automatic bookmark classification: A collaborative approach. In Proceedings of the Second Workshop on Innovations in Web Infrastructure (IWI 2006), Edinburgh, Scotland, 2006.
[6]
J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98). G. F. Cooper, and S. Moral, Eds. Morgan-Kaufmann, San Francisco, Calif, pages 43--52, 1998.
[7]
R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12:331--370, 2002.
[8]
M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining content-based and collaborative filters in an online newspaper. In Proceedings of ACM SIGIR Workshop on Recommender Systems, August 1999.
[9]
M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems. Springer-Verlag, 22/1, 2004.
[10]
D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61--70, 1992.
[11]
R. Jaeschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Tag recommendations in folksonomies. In Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Warsaw, Poland (to appear), 2007.
[12]
Q. Lin and B. M. Kim. An approach for combining content-based and collaborative filters. In Proceedings of the Sixth international workshop on Information retrieval with Asian languages (ACL-2003), pages 17--24, 2003.
[13]
L. B. Marinho and L. Schmidt-Thieme. Collaborative tag recommendations. In Proceedings of 31st Annual Conference of the Gesellschaft fr Klassifikation (GfKl), Freiburg (to appear). Springer, 2007.
[14]
P. Melville, R. Mooney, and R. Nagarajan. Content-boosted collaborative filtering. In Proceedings of Eighteenth National Conference on Artificial Intelligence (AAAI-2002), pages 187--192, 2001.
[15]
G. Mishne. Autotag: a collaborative approach to automated tag assignment for weblog posts. In WWW '06: Proceedings of the 15th international conference on World Wide Web, pages 953--954, New York, NY, USA, 2006. ACM Press.
[16]
M. J. Pazzani. A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev., 13(5--6):393--408, 1999.
[17]
A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pages 437--444, 2001.
[18]
P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the ACM1994 Conference on Computer Supported Cooperative Work, pages 175--186, Chapel Hill, North Carolina, 1994. ACM.
[19]
B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Analysis of recommendation algorithms for e-commerce. In Proceedings of the Second ACM Conference on Electronic Commerce (EC '00), pages 285--295, 2000.
[20]
K. Tso and L. Schmidt-Thieme. Evaluation of attribute-aware recommender system algorithms on data with varying characteristics. In Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006), pages 831--840. Springer, 2006.
[21]
J. Wang, A. P. de Vries, and M. J. T. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 501--508, New York, NY, USA, 2006. ACM Press.
[22]
Z. Xu, Y. Fu, J. Mao, and D. Su. Towards the semantic web: Collaborative tag suggestions. In Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006, Edinburgh, Scotland, 2006.

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  1. Tag-aware recommender systems by fusion of collaborative filtering algorithms

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    cover image ACM Conferences
    SAC '08: Proceedings of the 2008 ACM symposium on Applied computing
    March 2008
    2586 pages
    ISBN:9781595937537
    DOI:10.1145/1363686
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    Publication History

    Published: 16 March 2008

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    Author Tags

    1. collaborative filtering
    2. recommender systems
    3. tags

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    SAC '08: The 2008 ACM Symposium on Applied Computing
    March 16 - 20, 2008
    Fortaleza, Ceara, Brazil

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    View all
    • (2024)When Box Meets Graph Neural Network in Tag-aware RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671973(1770-1780)Online publication date: 25-Aug-2024
    • (2023)DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative FilteringApplied Sciences10.3390/app1305294513:5(2945)Online publication date: 24-Feb-2023
    • (2023)TRAL: A Tag-Aware Recommendation Algorithm Based on Attention LearningApplied Sciences10.3390/app1302081413:2(814)Online publication date: 6-Jan-2023
    • (2023)Content-Based Recommender Systems TaxonomyFoundations of Computing and Decision Sciences10.2478/fcds-2023-000948:2(211-241)Online publication date: 30-Jun-2023
    • (2023)Graph-Based Recommendation System Enhanced by Community DetectionScientific Programming10.1155/2023/50737692023Online publication date: 1-Jan-2023
    • (2023)Transient Group Recommendation for Shared Environment using Check-in and Tag Data2023 IEEE 6th International Conference on Knowledge Innovation and Invention (ICKII)10.1109/ICKII58656.2023.10332791(661-666)Online publication date: 11-Aug-2023
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    • (2021)Tagging Items Automatically Based on Both Content Information and Browsing BehaviorsINFORMS Journal on Computing10.1287/ijoc.2020.100733:3(882-897)Online publication date: 1-Jul-2021
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