July  2016, 1(2&3): 261-274. doi: 10.3934/bdia.2016008

Time aware topic based recommender system

1. 

Department of Electrical Engineering and Computer Science, York University, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada, Canada, Canada, Canada

Received  August 2016 Revised  October 2016 Published  November 2016

News recommender systems efficiently handle the overwhelming number of news articles, simplify navigations, and retrieve relevant information. Many conventional news recommender systems use collaborative filtering to make recommendations based on the behavior of users in the system. In this approach, the introduction of new users or new items can cause the cold start problem, as there will be insufficient data on these new entries for the collaborative filtering to draw any inferences for new users or items. Content-based news recommender systems emerged to address the cold start problem. However, many content-based news recommender systems consider documents as a bag-of-words neglecting the hidden themes of the news articles. In this paper, we propose a news recommender system leveraging topic models and time spent on each article. We build an automated recommender system that is able to filter news articles and make recommendations based on users' preferences. We use topic models to identify the thematic structure of the corpus. These themes are incorporated into a content-based recommender system to filter news articles that contain themes that are of less interest to users and to recommend articles that are thematically similar to users' preferences. Our experimental studies show that utilizing topic modeling and spent time on a single article can outperform the state of the arts recommendation techniques. The resulting recommender system based on the proposed method is currently operational at The Globe and Mail (http://www.theglobeandmail.com/).
Citation: Elnaz Delpisheh, Aijun An, Heidar Davoudi, Emad Gohari Boroujerdi. Time aware topic based recommender system. Big Data & Information Analytics, 2016, 1 (2&3) : 261-274. doi: 10.3934/bdia.2016008
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 (2005), 734-749. doi: 10.1109/TKDE.2005.99.

[2]

D. M. Andrzejewski, Incorporating Domain Knowledge in Latent Topic Models, PhD thesis, University of Wisconsin-Madison, USA, 2010.

[3]

D. M. Blei, A. Y. Ng and M. I. Jordan, Latent dirichlet allocation, The Journal of Machine Learning Research, 3 (2003), 993-1022.

[4]

J. Bobadilla, F. Ortega, A. Hernando and J. Bernal, A collaborative filtering approach to mitigate the new user cold start problem, Knowledge-Based System, 26 (2012), 225-238. doi: 10.1016/j.knosys.2011.07.021.

[5]

H. Borges and A. Lorena, A survey on recommender systems for news data, in Smart Information and Knowledge Management (eds. E. Szczerbicki and N. Nguyen), vol. 260 of Studies in Computational Intelligence, Springer Berlin Heidelberg, 2010, 129-151. doi: 10.1007/978-3-642-04584-4_6.

[6]

R. Burke, Hybrid recommender systems: Survey and experiments, User Modeling and User-Adapted Interaction, 12 (2002), 331-370. doi: 10.1023/A:1021240730564.

[7]

S.-H. Cha, Comprehensive survey on distance/similarity measures between probability density functions, International Journal of Mathematical Models and Methods in Applied Sciences, 1 (2007), 300-307.

[8]

T.-M. Chang and W.-F. Hsiao, Lda-based personalized document recommendation, Proceedings of the PACIS, 2013.

[9]

M. D. Ekstrand, J. T. Riedl and J. A. Konstan, Collaborative filtering recommender systems, Journal of Foundations and Trends in Human-Computer Interaction, 4 (2011), 81-173. doi: 10.1561/1100000009.

[10]

T. Griffiths, Gibbs sampling in the generative model of latent dirichlet allocation, Standford University, 518 (2002), 1-3.

[11]

T. L. Griffiths and M. Steyvers, Finding scientific topics, Proceeding of the National Academy of Sciences of the United States of America, 101 (2004), 5228-5235. doi: 10.1073/pnas.0307752101.

[12]

X. He, T. Chen, M.-Y. Kan and X. Chen, Trirank: Review-aware explainable recommendation by modeling aspects, in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM '15, ACM, New York, NY, USA, 2015, 1661-1670.

[13]

G. Heinrich, Parameter estimation for text analysis, http://www.arbylon.net/publications/text-est.pdf.

[14]

M. D. Hoffman, D. M. Blei and F. R. Bach, Online learning for latent dirichlet allocation, in NIPS (eds. J. D. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel and A. Culotta), Curran Associates, Inc., 2010, 856-864.

[15]

D. Jurafsky and J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 1st edition, Prentice Hall PTR, Upper Saddle River, NJ, USA, 2000.

[16]

Q. V. Le and T. Mikolov, Distributed representations of sentences and documents, CoRR, abs/1405.4053, URL http://arxiv.org/abs/1405.4053.

[17]

D. D. Lee and H. S. Seung, Algorithms for non-negative matrix factorization, in Advances in Neural Information Processing Systems 13 (eds. T. K. Leen, T. G. Dietterich and V. Tresp), MIT Press, 2001, 556-562, URL http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf.

[18]

G. Linden, B. Smith and J. York, Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Internet Computing, 7 (2003), 76-80. doi: 10.1109/MIC.2003.1167344.

[19]

C. D. Manning and H. Schütze, Foundations of Statistical Natural Language Processing, The MIT Press, Cambridge, Massachusetts, 1999.

[20]

A. K. McCallum, Mallet: A machine learning for language toolkit, 2002, http://mallet.cs.umass.edu.

[21]

T. Mikolov, I. Sutskever, K. Chen, G. Corrado and J. Dean, Distributed representations of words and phrases and their compositionality, CoRR, abs/1310.4546, URL http://arxiv.org/abs/1310.4546.

[22]

B. N. Miller, I. Albert, S. K. Lam, J. A. Konstan and J. Riedl, Movielens unplugged: Experiences with an occasionally connected recommender system, in Proceedings of the 8th International Conference on Intelligent User Interfaces, IUI '03, ACM, New York, NY, USA, 2003, 263-266. doi: 10.1145/604045.604094.

[23]

D. Z. Mária Bieliková Michal Kompan, Effective hierarchical vector-based news representation for personalized recommendation, Computer Science and Information Systems, 303-322, URL http://eudml.org/doc/252774.

[24]

F. Ricci, L. Rokach and B. Shapira, Recommender Systems Handbook, chapter Introduction to Recommender Systems Handbook, Springer US, Boston, MA, 2011.

[25]

S. Tuarob, L. C. Pouchard and C. L. Giles, Automatic tag recommendation for metadata annotation using probabilistic topic modeling, in Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '13, ACM, New York, NY, USA, 2013, 239-248. doi: 10.1145/2467696.2467706.

show all references

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 (2005), 734-749. doi: 10.1109/TKDE.2005.99.

[2]

D. M. Andrzejewski, Incorporating Domain Knowledge in Latent Topic Models, PhD thesis, University of Wisconsin-Madison, USA, 2010.

[3]

D. M. Blei, A. Y. Ng and M. I. Jordan, Latent dirichlet allocation, The Journal of Machine Learning Research, 3 (2003), 993-1022.

[4]

J. Bobadilla, F. Ortega, A. Hernando and J. Bernal, A collaborative filtering approach to mitigate the new user cold start problem, Knowledge-Based System, 26 (2012), 225-238. doi: 10.1016/j.knosys.2011.07.021.

[5]

H. Borges and A. Lorena, A survey on recommender systems for news data, in Smart Information and Knowledge Management (eds. E. Szczerbicki and N. Nguyen), vol. 260 of Studies in Computational Intelligence, Springer Berlin Heidelberg, 2010, 129-151. doi: 10.1007/978-3-642-04584-4_6.

[6]

R. Burke, Hybrid recommender systems: Survey and experiments, User Modeling and User-Adapted Interaction, 12 (2002), 331-370. doi: 10.1023/A:1021240730564.

[7]

S.-H. Cha, Comprehensive survey on distance/similarity measures between probability density functions, International Journal of Mathematical Models and Methods in Applied Sciences, 1 (2007), 300-307.

[8]

T.-M. Chang and W.-F. Hsiao, Lda-based personalized document recommendation, Proceedings of the PACIS, 2013.

[9]

M. D. Ekstrand, J. T. Riedl and J. A. Konstan, Collaborative filtering recommender systems, Journal of Foundations and Trends in Human-Computer Interaction, 4 (2011), 81-173. doi: 10.1561/1100000009.

[10]

T. Griffiths, Gibbs sampling in the generative model of latent dirichlet allocation, Standford University, 518 (2002), 1-3.

[11]

T. L. Griffiths and M. Steyvers, Finding scientific topics, Proceeding of the National Academy of Sciences of the United States of America, 101 (2004), 5228-5235. doi: 10.1073/pnas.0307752101.

[12]

X. He, T. Chen, M.-Y. Kan and X. Chen, Trirank: Review-aware explainable recommendation by modeling aspects, in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM '15, ACM, New York, NY, USA, 2015, 1661-1670.

[13]

G. Heinrich, Parameter estimation for text analysis, http://www.arbylon.net/publications/text-est.pdf.

[14]

M. D. Hoffman, D. M. Blei and F. R. Bach, Online learning for latent dirichlet allocation, in NIPS (eds. J. D. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel and A. Culotta), Curran Associates, Inc., 2010, 856-864.

[15]

D. Jurafsky and J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 1st edition, Prentice Hall PTR, Upper Saddle River, NJ, USA, 2000.

[16]

Q. V. Le and T. Mikolov, Distributed representations of sentences and documents, CoRR, abs/1405.4053, URL http://arxiv.org/abs/1405.4053.

[17]

D. D. Lee and H. S. Seung, Algorithms for non-negative matrix factorization, in Advances in Neural Information Processing Systems 13 (eds. T. K. Leen, T. G. Dietterich and V. Tresp), MIT Press, 2001, 556-562, URL http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf.

[18]

G. Linden, B. Smith and J. York, Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Internet Computing, 7 (2003), 76-80. doi: 10.1109/MIC.2003.1167344.

[19]

C. D. Manning and H. Schütze, Foundations of Statistical Natural Language Processing, The MIT Press, Cambridge, Massachusetts, 1999.

[20]

A. K. McCallum, Mallet: A machine learning for language toolkit, 2002, http://mallet.cs.umass.edu.

[21]

T. Mikolov, I. Sutskever, K. Chen, G. Corrado and J. Dean, Distributed representations of words and phrases and their compositionality, CoRR, abs/1310.4546, URL http://arxiv.org/abs/1310.4546.

[22]

B. N. Miller, I. Albert, S. K. Lam, J. A. Konstan and J. Riedl, Movielens unplugged: Experiences with an occasionally connected recommender system, in Proceedings of the 8th International Conference on Intelligent User Interfaces, IUI '03, ACM, New York, NY, USA, 2003, 263-266. doi: 10.1145/604045.604094.

[23]

D. Z. Mária Bieliková Michal Kompan, Effective hierarchical vector-based news representation for personalized recommendation, Computer Science and Information Systems, 303-322, URL http://eudml.org/doc/252774.

[24]

F. Ricci, L. Rokach and B. Shapira, Recommender Systems Handbook, chapter Introduction to Recommender Systems Handbook, Springer US, Boston, MA, 2011.

[25]

S. Tuarob, L. C. Pouchard and C. L. Giles, Automatic tag recommendation for metadata annotation using probabilistic topic modeling, in Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '13, ACM, New York, NY, USA, 2013, 239-248. doi: 10.1145/2467696.2467706.

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