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A Rule-Based Flickr Tag Recommendation System

  • Luca Cagliero
  • Alessandro Fiori
  • Luigi Grimaudo
Chapter
Part of the Computer Communications and Networks book series (CCN)

Abstract

Personalized tag recommendation focuses on helping users find desirable keywords (tags) to annotate Web resources based on both user profiles and main resource characteristics. Flickr is a popular online photo service whose resource sharing system significantly relies on annotations. However, recommending tags to a Flickr user who is annotating a photo is a challenging task as the lack of a controlled tag vocabulary makes the annotation history collection very sparse. This chapter presents a novel rule-based personalized tag recommendation system to suggest additional pertinent tags to partially annotated resources. Rules represent potentially valuable correlations among tag sets. Intuitively, the system should recommend tags highly correlated with the previously annotated tags. Unlike previous rule-based approaches, a Wordnet taxonomy is used to drive the rule mining process and discover rules, called generalized rules, that may contain either single tags or their semantically meaningful aggregations. The use of generalized rules in tag recommendation makes the system (1) more robust to data sparsity and (2) able to capture different viewpoints of the analyzed data. Experiments demonstrate the usefulness of generalized rules in recommending additional tags for real photos published on Flickr.

Keywords

Association Rule Recommendation System Rule Mining Minimum Support Threshold Photo Collection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Proceedings of the 2008 ACM conference on Recommender Systems, RecSys ’08, pp. 335–336. ACM, New York (2008). doi:http://doi.acm.org/10.1145/1454008.1454068, http://doi.acm.org/10.1145/1454008.1454068
  2. 2.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, pp. 207–216. ACM, New York (1993)Google Scholar
  3. 3.
    Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., Su, Z.: Optimizing web search using social annotations. In: Proceedings of the 16th International Conference on World Wide Web, WWW ’07, pp. 501–510. ACM, New York (2007). DOI http://doi.acm.org/10.1145/1242572.1242640, http://doi.acm.org/10.1145/1242572.1242640
  4. 4.
    Baralis, E., Cagliero, L., Cerquitelli, T., D’Elia, V., Garza, P.: Support driven opportunistic aggregation for generalized itemset extraction. In: IEEE Conference of Intelligent Systems, pp. 102–107 (2010)Google Scholar
  5. 5.
    Baralis, E., Cagliero, L., Cerquitelli, T., Garza, P., Marchetti, M.: Cas-mine: providing personalized services in context-aware applications by means of generalized rules. Knowl. Inf. Syst. 28(2), 283–310 (2011)CrossRefGoogle Scholar
  6. 6.
    Baralis, E., Cagliero, L., Cerquitelli, T., Garza, P.: Generalized association rule mining with constraints. Inf. Sci. 194, 68–84 (2012)CrossRefGoogle Scholar
  7. 7.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Proceedings of the Seventh International Conference on World Wide Web 7, pp. 107–117. Elsevier, Amsterdam/New York (1998)Google Scholar
  8. 8.
    Cagliero, L.: Discovering temporal change patterns in the presence of taxonomies. IEEE Trans. Knowl. Data Eng. 99 (2011, Preprints). doi:http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.233
  9. 9.
    Chirita, P.A., Costache, S., Nejdl, W., Handschuh, S.: P-tag: large scale automatic generation of personalized annotation tags for the web. In: Proceedings of the 16th International Conference on World Wide Web, WWW ’07, pp. 845–854. ACM, New York (2007). doi:http://doi.acm.org/10.1145/1242572.1242686, http://doi.acm.org/10.1145/1242572.1242686
  10. 10.
    Datta, R., Ge, W., Li, J., Wang, J.Z.: Toward bridging the annotation-retrieval gap in image search. IEEE MultiMed. 14, 24–35 (2007). doi:10.1109/MMUL.2007.67, http://dl.acm.org/citation.cfm?id=1435658.1436725 Google Scholar
  11. 11.
    Del.icio.us.: Del.icio.us. Website (2012). http://delicious.com
  12. 12.
    Dmitriev, P.A., Eiron, N., Fontoura, M., Shekita, E.: Using annotations in enterprise search. In: Proceedings of the 15th International Conference on World Wide Web, WWW ’06, pp. 811–817. ACM, New York (2006). doi:http://doi.acm.org/10.1145/1135777.1135900, http://doi.acm.org/10.1145/1135777.1135900
  13. 13.
    Elmasri, R., Navathe, S.B.: Fundamentals of Database Systems, 5th edn. Addison Wesley, Boston (2006). http://www.amazon.ca/exec/obidos/redirect?tag=citeulike04-20{\&}path=ASIN/0321369572Google Scholar
  14. 14.
    Flickr: Flickr Website (2012). http://www.flickr.com
  15. 15.
    Garg, N., Weber, I.: Personalized, interactive tag recommendation for flickr. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys ’08, pp. 67–74. ACM, New York (2008). doi:http://doi.acm.org/10.1145/1454008.1454020. http://doi.acm.org/10.1145/1454008.1454020
  16. 16.
    Han, J., Fu, Y.: Mining multiple-level association rules in large databases. IEEE Trans. Knowl. Data Eng. 11(5), 798–805 (2002)Google Scholar
  17. 17.
    Heymann, P., Ramage, D., Garcia-Molina, H.: Social tag prediction. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’08, pp. 531–538. ACM, New York (2008). doi:http://doi.acm.org/10.1145/1390334.1390425. http://doi.acm.org/10.1145/1390334.1390425
  18. 18.
    Jäschke, R., Marinho, L., Hotho, A., Lars, S.T., Gerd, S.: Tag recommendations in folksonomies. In: Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007, pp. 506–514. Springer, Berlin/Heidelberg (2007). doi:http://dx.doi.org/10.1007/978-3-540-74976-9_52, http://dx.doi.org/10.1007/978-3-540-74976-9_52
  19. 19.
    Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys ’09, pp. 61–68. ACM, New York (2009). doi:http://doi.acm.org/10.1145/1639714.1639726, http://doi.acm.org/10.1145/1639714.1639726
  20. 20.
    Lipczak, M., Milios, E.: Efficient tag recommendation for real-life data. ACM Trans. Intell. Syst. Technol. 3(1), 2:1–2:21 (2011). doi:10.1145/2036264.2036266, http://doi.acm.org/10.1145/2036264.2036266Google Scholar
  21. 21.
    Lops, P., Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, New York/London (2011)CrossRefGoogle Scholar
  22. 22.
    Lu, Y.T., Yu, S.I., Chang, T.C., Hsu, J.Y.j.: A content-based method to enhance tag recommendation. In: Proceedings of the 21st International Joint Conference on Artifical Intelligence, IJCAI’09, pp. 2064–2069. Morgan Kaufmann Publishers, San Francisco (2009). http://dl.acm.org/citation.cfm?id=1661445.1661775
  23. 23.
    Mishne, G.: Autotag: a collaborative approach to automated tag assignment for weblog posts. In: Proceedings of the 15th International Conference on World Wide Web, WWW ’06, pp. 953–954. ACM, New York (2006). doi:http://doi.acm.org/10.1145/1135777.1135961, http://doi.acm.org/10.1145/1135777.1135961
  24. 24.
    Pramudiono, I., Kitsuregawa, M.: Fp-tax: tree structure based generalized association rule mining. In: Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, p. 63. ACM, New York (2004)Google Scholar
  25. 25.
    Rae, A., Sigurbjörnsson, B., van Zwol, R.: Improving tag recommendation using social networks. In: Adaptivity, Personalization and Fusion of Heterogeneous Information, RIAO ’10, pp. 92–99. Le centre de Haute Etudes Internationales d’Informatique Documentaire, Paris (2010). http://dl.acm.org/citation.cfm?id=1937055.1937077
  26. 26.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, New York/London (2011)zbMATHGoogle Scholar
  27. 27.
    Sanderson, M., Zobel, J.: Information retrieval system evaluation: effort, sensitivity, and reliability. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’05, pp. 162–169. ACM, New York (2005). doi:http://doi.acm.org/10.1145/1076034.1076064, http://doi.acm.org/10.1145/1076034.1076064
  28. 28.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW ’01, pp. 285–295. ACM, New York (2001). doi:http://doi.acm.org/10.1145/371920.372071. http://doi.acm.org/10.1145/371920.372071
  29. 29.
    Sigurbjörnsson, B., van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: Proceedings of the 17th International Conference on World Wide Web, WWW ’08, pp. 327–336. ACM, New York (2008). doi:http://doi.acm.org/10.1145/1367497.1367542. http://doi.acm.org/10.1145/1367497.1367542
  30. 30.
    Srikant, R., Agrawal, R.: Mining generalized association rules. In: Proceedings of the International Conference on Very Large Data Bases, pp. 407–419. Morgan Kaufmann, San Fransisco (1995)Google Scholar
  31. 31.
    Srikant, R., Vu, Q., Agrawal, R.: Mining association rules with item constraints. In: Proceedings of the Conference on Knowledge Discovery and Data Mining, vol. 97, pp. 67–73. AAAI Press, Menlo Park (1997)Google Scholar
  32. 32.
    Sriphaew, K., Theeramunkong, T.: A new method for finding generalized frequent itemsets in generalized association rule mining. In: Seventh International Symposium on Computers and Communications, pp. 1040–1045. IEEE, Washington, DC (2002)Google Scholar
  33. 33.
    Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys ’08, pp. 43–50. ACM, New York (2008). doi:http://doi.acm.org/10.1145/1454008.1454017, http://doi.acm.org/10.1145/1454008.1454017
  34. 34.
    van Erp, M., Schomaker, L.: Variants of the borda count method for combining ranked classifier hypotheses. In: Proceedings of the 7th International Workshop on Frontiers in Handwriting Recognition, Amsterdam, pp. 443–452 (2000)Google Scholar
  35. 35.
    Wordnet: Wordnet Lexical Database (2012). http://wordnet.princeton.edu
  36. 36.
    Zooomr: Zooomr Website (2012). http://www.zooomr.com

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Luca Cagliero
    • 1
  • Alessandro Fiori
    • 1
  • Luigi Grimaudo
    • 1
  1. 1.Politecnico di Torino. Corso Duca degli AbruzziTorinoItaly

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