Abstract
Albeit the implicit feedback based recommendation problem—when only the user history is available but there are no ratings—is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few implicit feedback benchmark datasets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS applies a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. The method also allows us to incorporate various contextual information into the model while maintaining its computational efficiency. We present two context-aware implementation variants of iTALS. The first incorporates seasonality and enables to distinguish user behavior in different time intervals. The other views the user history as sequential information and has the ability to recognize usage pattern typical to certain group of items, e.g. to automatically tell apart product types that are typically purchased repetitively or once. Experiments performed on five implicit datasets (LastFM 1K, Grocery, VoD, and “implicitized” Netflix and MovieLens 10M) show that by integrating context-aware information with our factorization framework into the state-of-the-art implicit recommender algorithm the recommendation quality improves significantly.
Chapter PDF
Similar content being viewed by others
References
Pilászy, I., Tikk, D.: Recommending new movies: Even a few ratings are more valuable than metadata. In: Recsys 2009: ACM Conf. on Recommender Systems, New York, NY, USA, pp. 93–100 (2009)
Bennett, J., Lanning, S.: The Netflix Prize. In: KDD Cup Workshop at SIGKDD 2007, San Jose, California, USA, pp. 3–6 (2007)
Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: ICDM 2007: IEEE Int. Conf. on Data Mining, Omaha, NE, USA, pp. 43–52 (2007)
Takács, G., Pilászy, I., Németh, B., Tikk, D.: Major components of the Gravity recommendation system. SIGKDD Explor. Newsl. 9, 80–83 (2007)
Pilászy, I., Zibriczky, D., Tikk, D.: Fast ALS-based matrix factorization for explicit and implicit feedback datasets. In: Recsys 2010: ACM Conf. on Recommender Systems, Barcelona, Spain, pp. 71–78 (2010)
Takács, G., Pilászy, I., Tikk, D.: Applications of the conjugate gradient method for implicit feedback collaborative filtering. In: RecSys 2011: ACM Conf. on Recommender Systems, Chicago, IL, USA, pp. 297–300 (2011)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: SIGKDD 2008: ACM Int. Conf. on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, pp. 426–434 (2008)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems 20. MIT Press, Cambridge (2008)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp. 1–35. Springer, US (2011)
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recsys 2008: ACM Conf. on Recommender Systems, Lausanne, Switzerland, pp. 335–336 (2008)
Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. In: Recsys 2010: ACM Conf. on Recommender Systems, Barcelona, Spain, pp. 79–86 (2010)
Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. 23(1), 103–145 (2005)
Adomavicius, G., Ricci, F.: Workshop on context-aware recommender systems (CARS-2009). In: Recsys 2009: ACM Conf. on Recommender Systems, New York, NY, USA, pp. 423–424 (2009)
Said, A., Berkovsky, S., De Luca, E.W.: Putting things in context: Challenge on context-aware movie recommendation. In: CAMRa 2010: Workshop on Context-Aware Movie Recommendation, Barcelona, Spain, pp. 2–6 (2010)
Bogers, T.: Movie recommendation using random walks over the contextual graph. In: CARS 2010: 2nd Workshop on Context-Aware Recommender Systems, Barcelona, Spain, pp. 1–5 (2010)
Baltrunas, L., Amatriain, X.: Towards time-dependant recommendation based on implicit feedback. In: CARS 2009: Workshop on Context-aware Recommender Systems, New York, NY, USA, pp. 1–5 (2009)
Bader, R., Neufeld, E., Woerndl, W., Prinz, V.: Context-aware POI recommendations in an automotive scenario using multi-criteria decision making methods. In: CaRR 2011: Workshop on Context-awareness in Retrieval and Recommendation, Palo Alto, CA, USA, pp. 23–30 (2011)
He, Q., Pei, J., Kifer, D., Mitra, P., Giles, L.: Context-aware citation recommendation. In: WWW 2010: Int. Conf. on World Wide Web, Raleigh, NC, USA, pp. 421–430 (2010)
Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., Pedone, A.: Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. In: Recsys 2009: ACM Conf. on Recommender Systems, New York, NY, USA, pp. 265–268 (2009)
Lathauwer, L.D., Moor, B.D., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000)
Rendle, S., Gantner, Z., Freudenthaler, C., Schmidt-Thieme, L.: Fast context-aware recommendations with factorization machines. In: SIGIR 2011: ACM Int. Conf. on Research and Development in Information, Beijing, China, pp. 635–644 (2011)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM 2008: IEEE Int. Conf. on Data Mining, Pisa, Italy, pp. 263–272 (2008)
Liu, N.N., Cao, B., Zhao, M., Yang, Q.: Adapting neighborhood and matrix factorization models for context aware recommendation. In: CAMRa 2010: Workshop on Context-Aware Movie Recommendation, Barcelona, Spain, pp. 7–13 (2010)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD 1993: ACM SIGMOD Int. Conf. on Management of Data, Washington DC, USA, pp. 207–216 (1993)
Davidson, J., Liebald, B., Liu, J., et al.: The YouTube video recommendation system. In: Recsys 2010: ACM Conf. on Recommender Systems, Barcelona, Spain, pp. 293–296 (2010)
Celma, O.: Music Recommendation and Discovery in the Long Tail. Springer (2010)
GroupLens Research: Movielens data sets (2006), http://www.grouplens.org/node/73
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hidasi, B., Tikk, D. (2012). Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Implicit Feedback. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33486-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-33486-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33485-6
Online ISBN: 978-3-642-33486-3
eBook Packages: Computer ScienceComputer Science (R0)