Skip to main content
SpringerLink
Log in

Interactive resource recommendation algorithm based on tag information

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

With the popularization of social media and the exponential growth of information generated by online users, the recommender system has been popular in helping users to find the desired resources from vast amounts of data. However, the cold-start problem is one of the major challenges for personalized recommendation. In this work, we utilized the tag information associated with different resources, and proposed a tag-based interactive framework to make the resource recommendation for different users. During the interaction, the most effective tag information will be selected for users to choose, and the approach considers the users’ feedback to dynamically adjusts the recommended candidates during the recommendation process. Furthermore, to effectively explore the user preference and resource characteristics, we analyzed the tag information of different resources to represent the user and resource features, considering the users’ personal operations and time factor, based on which we can identify the similar users and resource items. Probabilistic matrix factorization is employed in our work to overcome the rating sparsity, which is enhanced by embedding the similar user and resource information. The experiments on real-world datasets demonstrate that the proposed algorithm can get more accurate predictions and higher recommendation efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Similar content being viewed by others

Notes

  1. Some tag categories may fail to fully divide the entire resource set due to miss tagging, so we only consider those categories covering all resource items

References

  1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)

    Article  Google Scholar 

  2. Ali, S.M., Ghani, I., Latiff, M.S.A.: Interaction-based collaborative recommendation: a personalized learning environment (ple) perspective. Trans. Internet Inf. Syst. 9, 446–465 (2015)

    Google Scholar 

  3. Barkan, O., Koenigstein, N.: Item2vec: neural item embedding for collaborative filtering. In: The 26th IEEE International Workshop on Machine Learning for Signal Processing, pp 1–6 (2016)

  4. Bennett, J., Lanning, S., Netflix, N.: The netflix prize. In: KDD Cup and Workshop in Conjunction with KDD (2009)

  5. Chatti, M.A., Dakova, S., Thus, H., Schroeder, U.: Tag-based collaborative filtering recommendation in personal learning environments. IEEE Trans. Learn. Technol. 6, 337–349 (2013)

    Article  Google Scholar 

  6. Cheng, Y., Qiu, G., Bu, J., Liu, K., Han, Y., Wang, C., Chen, C.: Model bloggers’ interests based on forgetting mechanism. In: International Conference on World Wide Web, pp 1129–1130 (2008)

  7. De Gemmis, M., Lops, P., Semeraro, G., Basile, P.: Integrating tags in a semantic content-based recommender. In: ACM Conference on Recommender Systems (2008)

  8. Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)

    Article  Google Scholar 

  9. Firan, C.S., Nejdl, W., Paiu, R.: The benefit of using tag-based profiles. In: American Web Conference, pp 32–41 (2007)

  10. Guo, L., Ma, J., Chen, Z.: A social recommendation algorithm based on the association between recommended objects. Journal of Computers 37(1), 219–228 (2014)

    Google Scholar 

  11. Han, T., Liu, Y., Xie, Q.: Tagtour: a personalized tourist resource recommendation system. In: The 18th Asia-Pacific Web Conference (2016)

    Chapter  Google Scholar 

  12. He, T., Chen, Z., Liu, J., Zhou, X., Du, X., Wang, W.: An empirical study on user-topic rating based collaborative filtering methods. World Wide Web: Internet and Web Information Systems 20, 815–829 (2017)

    Article  Google Scholar 

  13. Hotho, A., Jaschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: search and ranking. In: European Semantic Web Conference, pp 411–426 (2006)

  14. Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: The 4th ACM Conference on Recommender Systems, pp 135–142 (2010)

  15. Jin, J., Chen, Q.: A trust-based top-k recommender system using social tagging network. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp 1270–1274 (2012)

  16. Kuramoto, I., Yasuda, A., Minakuchi, M., Tsujino, Y.: Recommendation system based on interaction with multiple agents for users with vague intention. In: International Conference on Human Computer Interaction, pp 351–357 (2011)

    Google Scholar 

  17. Li, J., Lu, K., Huang, Z., Shen, H.T.: Two birds one stone: on both cold-start and long-tail recommendation. In: The 25th ACM International Conference on Multimedia (2017)

  18. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2010)

    Article  Google Scholar 

  19. Liu, N., Jiang, Q., Chen, H., Wang, B.: Personalized recommendation using implicit interaction information. In: IEEE International Conference on Computer Science & Education, pp 1340–1345 (2011)

  20. Liu, S., Liu, Y., Xie, Q.: Personalized resource recommendation based on regular tag and user operation. In: The 18th Asia-Pacific Web Conference (2016)

    Chapter  Google Scholar 

  21. Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 203–210 (2009)

  22. Mathes, A.: Folksonomies - cooperative classification and communication through shared matadata. Comput.-Mediat. Commun. 47(10), 1–13 (2004)

    Google Scholar 

  23. Mishne, G.: Autotag: a collaborative approach to automated tag assignment for weblog posts. In: International Conference on World Wide Web (2006)

  24. Nepal, S., Paris, C., Freyne, P.A.P.J., Bista, S.K.: Interaction based content recommendation in online communities. In: International Conference on User Modeling, Adaptation and Personalization, pp 14–24 (2013)

  25. Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: The 21st Annual Conference on Neural Information Processing System, pp 1257–1264 (2007)

  26. Sarwar, B.M., Karypis, G., Konstan, J. A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: The 10th International Conference on World Wide Web, pp 285–295 (2001)

  27. Shan, H., Banerjee, A.: Generalized probabilistic matrix factorizations for collaborative filtering. In: The IEEE 10th International Conference on Data Mining, pp 1025–1030 (2010)

  28. Song, Y., Zhang, L., Giles, C.L.: Automatic tag recommendation algorithms for social recommender systems. ACM Trans. Web 5, 4:1–4:31 (2011)

    Article  Google Scholar 

  29. Sun, G., Liu, G., Zhao, L., Xu, J., Liu, A., Zhou, X.: A social trust path recommendation system in contextual online social networks. In: Proceedings of 16th Asia-Pacific Web Conference, pp 652–656 (2014)

    Google Scholar 

  30. Wartena, C., Brussee, R., Wibbels, M.: Using tag co-occurrence for recommendation. In: The 9th International Conference on Intelligent Systems Design and Applications (2009)

  31. Wu, Y., Yao, Y., Xu, F., Tong, H., Lu, J.: Tag2word: using tags to generate words for content based tag recommendation. In: The 25th ACM International Conference on Information and Knowledge Management, pp 2287–2292 (2016)

  32. Xiong, F., Liu, Y., Xie, Q.: Recommendations based on collaborative filtering by tag weights. In: The 13th International Conference on Semantics, Knowledge and Grids on Big Data (2017)

  33. Yang, X., Steck, H., Guo, Y., Liu, Y.: On top-k recommendation using social networks. In: The 6th ACM Conference on Recommender Systems, pp 67–74 (2012)

  34. Yang, X., Steck, H., Liu, Y.: Circle-based recommendation in online social networks. In: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1267–1275 (2012)

  35. Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Hung, N.Q.V.: Adapting to user interest drift for poi recommendation. IEEE Trans. Knowl. Data Eng. 28, 2566–2581 (2016)

    Article  Google Scholar 

  36. Zhang, F., Yuan, N.J., Zheng, K., Lian, D., Xie, X., Rui, Y.: Exploiting dining preference for restaurant recommendation. In: The 25th World Wide Web Conference (2016)

  37. Zhang, J., Peng, Q., Sun, S., Liu, C.: Collaborative filtering recommendation algorithm based on user preference derived from item domain features. Physica A: Statistical Mechanics and Its Applications 296, 66–76 (2014)

    Article  Google Scholar 

  38. Zheng, B., Su, H., Zheng, K., Zhou, X.: Landmark-based route recommendation with crowd intelligence. Data Science and Engineering 1, 86–100 (2016)

    Article  Google Scholar 

  39. Zheng, K., Su, H., Zheng, B., Shang, S., Xu, J., Liu, J., Zhou, X.: Interactive top-k spatial keyword queries. In: The 31st IEEE International Conference on Data Engineering (2015)

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China

    Qing Xie, Feng Xiong, Tian Han, Yongjian Liu & Lin Li

  2. School of Computer Science and Information Technology, RMIT University, Melbourne, Australia

    Zhifeng Bao

Authors
  1. Qing Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tian Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongjian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhifeng Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qing Xie.

Additional information

This article belongs to the Topical Collection: Special Issue on Deep Mining Big Social Data

Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell

Part of the results in this work appeared in proceedings of the 13th International Conference on Semantics, Knowledge and Grids on Big Data [32].

This research is partially supported by Natural Science Foundation of China (Grant No.61602353), National Social Science Foundation of China (Grant No.15BGL048) and the Fundamental Research Funds for the Central Universities (WUT:2017IVA053, WUT:2017IVB028 and WUT:2017II39GX).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, Q., Xiong, F., Han, T. et al. Interactive resource recommendation algorithm based on tag information. World Wide Web 21, 1655–1673 (2018). https://doi.org/10.1007/s11280-018-0532-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-018-0532-y

Keywords

Search

Navigation