A Large-Scale, Hybrid Approach for Recommending Pages Based on Previous User Click Pattern and Content

  • Mohammad Amir Sharif
  • Vijay V. Raghavan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)


In a large-scale recommendation setting, item-based collaborative filtering is preferable due to the availability of huge number of users’ preference information and relative stability in item-item similarity. Item-based collaborative filtering only uses users’ items preference information to predict recommendation for targeted users. This process may not always be effective, if the amount of preference information available is very small. For this kind of problem, item-content based similarity plays important role in addition to item co-occurrence-based similarity. In this paper we propose and evaluate a Map-Reduce based, large-scale, hybrid collaborative algorithm to incorporate both the content similarity and co-occurrence similarity. To generate recommendation for users having more or less preference information the relative weights of the item-item content-based and co-occurrence-based similarities are user-dependently tuned. Our experimental results on Yahoo! Front Page “Today Module User Click Log” dataset shows that we are able to get significant average precision improvement using the proposed method for user-dependent parametric incorporation of the two similarity metrics compared to other recent cited work.


Recommender Systems Item-based Collaborative Filtering Map- Reduce Item-Item content-based similarity Item-Item co-occurrence-based similarity Mahout 


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  1. 1.
    Hanani, U., Shapira, B., Shoval, P.: Information Filtering: Overview of Issues, Research and Systems. User Modeling and User-Adapted Interaction 11, 203–259 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Delgado, J., Ishii, N., Ura, T.: Content-based Collaborative Information Filtering: Actively Learning to Classify and Recommend Documents. In: Klusch, M., Weiss, G. (eds.) CIA 1998. LNCS (LNAI), vol. 1435, pp. 206–215. Springer, Heidelberg (1998)Google Scholar
  3. 3.
    Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 11(6), 734–749 (2005)CrossRefGoogle Scholar
  4. 4.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Journal of Advances in Artificial Intelligence (2009)Google Scholar
  5. 5.
    Hu, R., Lu, Y.: A Hybrid User and Item-based Collaborative Filtering with Smoothing on Sparse Data. In: Proceedings of the 16th International Conference on Artificial Reality and Telexistence–Workshops (2006)Google Scholar
  6. 6.
    Gong, S.J., Ye, H.W., Shi, X.Y.: A Collaborative Recommender Combining Item Rating Similarity and Item Attribute Similarity. International Seminar on Business and Information Management (2008)Google Scholar
  7. 7.
    Puntheeranurak, S., Chaiwitooanukool, T.: An Item-based Collaborative Filtering Method using Item-based Hybrid Similarity. In: 2nd International Conference on Software Engineering and Service Science (2011)Google Scholar
  8. 8.
    Jiang, J., Lu, J., Zhang, G., Long, G.: Scaling-up Item-based Collaborative Filtering Recommendation Algorithm based on Hadoop. IEEE World Congress on Services (2011)Google Scholar
  9. 9.
    Chen, Y., Pavlov, Y.: Large Scale Behavioral Targeting. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2009)Google Scholar
  10. 10.
  11. 11.
  12. 12.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mohammad Amir Sharif
    • 1
  • Vijay V. Raghavan
    • 1
  1. 1.The Center for Advanced Computer StudiesUniversity of Louisiana at LafayetteLafayetteUSA

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