Stacking recommendation engines with additional meta-features

ABSTRACT

In this paper, we apply stacking, an ensemble learning method, to the problem of building hybrid recommendation systems. We also introduce the novel idea of using runtime metrics which represent properties of the input users/items as additional meta-features, allowing us to combine component recommendation engines at runtime based on user/item characteristics. In our system, component engines are level-1 predictors, and a level-2 predictor is learned to generate the final prediction of the hybrid system. The input features of the level-2 predictor are predictions from component engines and the runtime metrics. Experimental results show that our system outperforms each single component engine as well as a static hybrid system. Our method has the additional advantage of removing restrictions on component engines that can be employed; any engine applicable to the target recommendation task can be easily plugged into the system.

REFERENCES

• 1: G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transaction on Knowledge and Data Engineering, 17(6), 2005.
• 2: J. Alspector, A. Kolcz and N. Karunanithi. Feature-based and clique-based user models for movie selection: A comparative study. User Modeling and User-Adapted Interaction, 7(4): 279--304, 1997.
• 3: J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering. In Proceedings of the 21st International Conference on Machine Learning (ICML), 2004.
• 4: C. Basu, H. Hirsh, and W. Cohen. Recommendation as classification: Using social and content-based information in recommendation. Recommender Systems. Papers from 1998 Workshop, Technical Report WS-98-08, 1998.
• 5: R. Bell, Y. Koren, and C. Volinsky. The bellkor solution to the netflix prize. KorBell Team's Report to Netflix, 2007.
• 6: R. Bell, Y. Koren, and C. Volinsky. Chasing $1,000,000: How we won the netflix progress prize. Statistical Computing and Statistical Graphics Newsletter, 18(2):4--12, 2007.
• 7: D. Billsus and M. J. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10(2--3):147 -- 180, 2000.
• 8: L. Breiman. Stacked regressions. Machine Learning, 24:49--64, 1996.
• 9: R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331--370, November 2002.
• 10: 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 the ACM SIGIR '99 Workshop Recommender Systems: Algorithms and Evaluation, August 1999.
• 11: S. Dzeroski and B. Zenko. Is combining classifiers with stacking better than selecting the best one? Machine Learning, 54(3):255--273, 2004.
• 12: T. Hastie, R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning. Springer, 2001.
• 13: J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 1999 Conference on Research and Development in Information Retrieval, pages 230--237, 1999.
• 14: IMDb. Internet movie database. downloadable at http://www.imdb.com/interfaces, 2008.
• 15: J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl. Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM, 40(3):77--87, 1997.
• 16: K. Lang. Newsweeder: Learning to filter netnews. In Proceedings of the 12th International Machine Learning Conference (ML--95), pages 331--339, 1995.
• 17: Lucene. Apache lucene. http://lucene.apache.org/, 2008.
• 18: MovieLens. http://www.grouplens.org/node/73, 1997.
• 19: Netflix prize, http://www.netflixprize.com/.
• 20: M. J. Pazzani. A framework for collaborative, content-based, and demographic filtering. Artificial Intelligence Review, 13(5-6):393--408, 1999.
• 21: M. Ramezani, L. Bergman, R. Thompson, R. Burke, and B. Mobasher. Selecting and applying recommendation technology. In IUI-08 Workshop on Recommendation and Collaboration (ReColl2008), 2008.
• 22: G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, 1986.
• 23: B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference, pages 285--295, 2001.
• 24: Y. Wang and I. H. Witten. Inducing model trees for continuous classes. In Proceedings of the 9th European Conference on Machine Learning, pages 128--137, 1997.
• 25: I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques, 2nd Edition. Morgan Kaufmann, San Francisco, CA, USA, 2005.
• 26: D. H. Wolpert. Stacked generalization. Neural Networks, 5(2):241--259, 1992.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models; Selection process

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Design

Keywords:
collaborative filtering, content-based recommender, engine hybridization, ensemble learning, hybrid recommender systems, machine learning, meta-feature, stacked generalization, stacking