Contextual Recommender Systems Using a Multidimensional Approach

Mohammed Mahmudur Rahman

doi:doi:10.11648/j.ijiis.20130204.11

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Contextual Recommender Systems Using a Multidimensional Approach

Mohammed Mahmudur Rahman

Published in International Journal of Intelligent Information Systems (Volume 2, Issue 4)

Received: 16 July 2013 Published: 20 August 2013

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Abstract

Recommender systems use the past experiences and preferences of the target users as a basis to provide personalized recommendations for them and as the same time, solve the information overloading problem. Context as the dynamic information describing the situation of items and users and affecting the user’s decision process is essential to be used by recommender systems. Multidimensional approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, profiling information, and hierarchical aggregation of recommendation. The recommender system could simultaneously possess the advantages of content-based recommendation, knowledge-based recommendation, collaborative filtering recommendation and On-Line Analytical Processing (OLAP) in segmenting the information. Following the improvement of the recommendation structure, it doesn’t have to limit its analysis on the user and product to compute for the recommendation result and it could also handle and determine more complex contextual information as recommendation computation foundation. It could develop better results if applied in different domains. This work extends the multidimensional recommendation model concept of Adomavicius and Tuzhilin (2001) and proposes a multidimensional recommendation environment to integrate the contextual information.

Published in	International Journal of Intelligent Information Systems (Volume 2, Issue 4)
DOI	10.11648/j.ijiis.20130204.11
Page(s)	55-63
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Aggregation, Contextual, Multidimensional, Recommendation

References

[1]	Adomavicius, G. & Tuzhilin, A. 2001. Extending recommender systems: A multidimensional approach. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-01), Workshop on Intelligent Techniques for Web Personalization, Seattle.
[2]	Adomavicius, G., Sankaranarayanan, R., Sen, S. & Tuzhilin, A. 2005. Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach, ACM Transactions on Information Systems, 23(1), 103-145.
[3]	Baltrunas, Linas, et al. "Context relevance assessment and exploitation in mobile recommender systems." Personal and Ubiquitous Computing 16.5 (2012): 507-526.
[4]	Baltrunas, Linas. "Exploiting contextual information in recommender systems."Proceedings of the 2008 ACM conference on Recommender systems. ACM, 2008.
[5]	Balabanovic, M. & Shoham, Y. 1997. Fab: Content-based, Collaborative Recommendation, Communications of the ACM, 40(3), 66-72.
[6]	Basu, C., Hirsh, H. & Cohen, W. 1998. Recommendation as Classification: Using Social and Content-based Information in Recommendation, Proceedings of the 15th National Conference on Artificial Intelligence, Madison, pp. 714-720.
[7]	Billsus, D. & Pazzani, M. J. 1998. Learning Collaborative Information Filters, Proceedings of the 15th International Conference on Machine Learning, San Francisco, pp. 46-54.
[8]	Breeze, J. S., Heckerman, D. & Kadie C. 1998. Empirical analysis of predictive algorithms for collaborative filtering, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, Madison, pp.43-52.
[9]	Brown, P. J., Bovey, J. D. & Chen, X. 1997. Context-aware Applications: From the Laboratory to the Marketplace, Proceedings of IEEE Personal Communications, 4(5), 58-64.
[10]	Goldberg, D., Nichols, D., Oki, B. & Terry, D. 1992. Using collaborative filtering to weave an information Tapestry, CACM, 35(12), 61-70.
[11]	Herlocker, J. L. & Konstan, J. A. 2001. Content-Independent Task-Focused Recommendation, IEEE Internet Computing, 5(6), 40-47.
[12]	Herlocker, J. L., Konstan, J., Terveen, L. & Riedl, J. 2004. Evaluating Collaborative Filtering Recommender Systems, ACM Transactions on Information Systems, 22(1), 5-53.
[13]	Karatzoglou, Alexandros, Xavier Amatriain, Linas Baltrunas, and Nuria Oliver. "Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering." In Proceedings of the fourth ACM conference on Recommender systems, pp. 79-86. ACM, 2010.
[14]	Liu, Liwei, et al. "Using context similarity for service recommendation."Semantic Computing (ICSC), 2010 IEEE Fourth International Conference on. IEEE, 2010.
[15]	Li, Y., Lu, L. & Xuefeng, L. 2005. A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce, Expert Systems with Applications, 28(1), 67-77.
[16]	Linden, G., Smith, B. & York, J. 2003. Amazon.com Recommendations Item to Item Collaborative Filtering, IEEE Internet Computing, 7(1), 2003, 76-80.
[17]	Pazzani, M. 1999. A Framework for Collaborative, Content-Based and Demographic Filtering, Artificial Intelligence Review, 13(5-6), 393-408.
[18]	Panniello, Umberto, et al. "Experimental comparison of pre-vs. post-filtering approaches in context-aware recommender systems." Proceedings of the third ACM conference on Recommender systems. ACM, 2009.
[19]	Ricci, Francesco, Lior Rokach, and Bracha Shapira. "Introduction to recommender systems handbook." Recommender Systems Handbook. Springer US, 2011. 1-35.
[20]	Said, Alan. "Identifying and utilizing contextual data in hybrid recommender systems." Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.
[21]	Sarwar, B., Karypis, G., Konstan, J. & Riedl, J. 2001. Item-based Collaborative Filtering Recommendation Algorithm, Proceedings of the 10th International WWW Conference, Hong Kong, pp. 285-295.
[22]	Shih, Y. Y. & Liu, D. R. 2005. Hybrid Recommendation Approaches: Collaborative Filtering via Valuable Content Information, Proceedings of 38th Hawaii International Conference on System Sciences, Big Island.
[23]	Tso-Sutter, Karen HL, Leandro Balby Marinho, and Lars Schmidt-Thieme. "Tag-aware recommender systems by fusion of collaborative filtering algorithms."Proceedings of the 2008 ACM symposium on Applied computing. ACM, 2008.
[24]	Woerndl, Wolfgang, Christian Schueller, and Rolf Wojtech. "A hybrid recommender system for context-aware recommendations of mobile applications." Data Engineering Workshop, 2007 IEEE 23rd International Conference on. IEEE, 2007.
[25]	Yang, W., Wang, Z. & You, M. 2004. An Improved Collaborative Filtering Method for Recommendations Generation, Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics, The Hague, Vol. 5, pp. 4135-4139.
[26]	Yujie, Zhang, and Wang Licai. "Some challenges for context-aware recommender systems." Computer Science and Education (ICCSE), 2010 5th International Conference on. IEEE, 2010.

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Mohammed Mahmudur Rahman. (2013). Contextual Recommender Systems Using a Multidimensional Approach. International Journal of Intelligent Information Systems, 2(4), 55-63. https://doi.org/10.11648/j.ijiis.20130204.11

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ACS Style

Mohammed Mahmudur Rahman. Contextual Recommender Systems Using a Multidimensional Approach. Int. J. Intell. Inf. Syst. 2013, 2(4), 55-63. doi: 10.11648/j.ijiis.20130204.11

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Mohammed Mahmudur Rahman. Contextual Recommender Systems Using a Multidimensional Approach. Int J Intell Inf Syst. 2013;2(4):55-63. doi: 10.11648/j.ijiis.20130204.11

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@article{10.11648/j.ijiis.20130204.11,
  author = {Mohammed Mahmudur Rahman},
  title = {Contextual Recommender Systems Using a Multidimensional Approach},
  journal = {International Journal of Intelligent Information Systems},
  volume = {2},
  number = {4},
  pages = {55-63},
  doi = {10.11648/j.ijiis.20130204.11},
  url = {https://doi.org/10.11648/j.ijiis.20130204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20130204.11},
  abstract = {Recommender systems use the past experiences and preferences of the target users as a basis to provide personalized recommendations for them and as the same time, solve the information overloading problem. Context as the dynamic information describing the situation of items and users and affecting the user’s decision process is essential to be used by recommender systems. Multidimensional approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, profiling information, and hierarchical aggregation of recommendation. The recommender system could simultaneously possess the advantages of content-based recommendation, knowledge-based recommendation, collaborative filtering recommendation and On-Line Analytical Processing (OLAP) in segmenting the information. Following the improvement of the recommendation structure, it doesn’t have to limit its analysis on the user and product to compute for the recommendation result and it could also handle and determine more complex contextual information as recommendation computation foundation. It could develop better results if applied in different domains. This work extends the multidimensional recommendation model concept of Adomavicius and Tuzhilin (2001) and proposes a multidimensional recommendation environment to integrate the contextual information.},
 year = {2013}
}

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TY  - JOUR
T1  - Contextual Recommender Systems Using a Multidimensional Approach
AU  - Mohammed Mahmudur Rahman
Y1  - 2013/08/20
PY  - 2013
N1  - https://doi.org/10.11648/j.ijiis.20130204.11
DO  - 10.11648/j.ijiis.20130204.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 55
EP  - 63
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20130204.11
AB  - Recommender systems use the past experiences and preferences of the target users as a basis to provide personalized recommendations for them and as the same time, solve the information overloading problem. Context as the dynamic information describing the situation of items and users and affecting the user’s decision process is essential to be used by recommender systems. Multidimensional approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, profiling information, and hierarchical aggregation of recommendation. The recommender system could simultaneously possess the advantages of content-based recommendation, knowledge-based recommendation, collaborative filtering recommendation and On-Line Analytical Processing (OLAP) in segmenting the information. Following the improvement of the recommendation structure, it doesn’t have to limit its analysis on the user and product to compute for the recommendation result and it could also handle and determine more complex contextual information as recommendation computation foundation. It could develop better results if applied in different domains. This work extends the multidimensional recommendation model concept of Adomavicius and Tuzhilin (2001) and proposes a multidimensional recommendation environment to integrate the contextual information.
VL  - 2
IS  - 4
ER  -

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Author Information

Mohammed Mahmudur Rahman

Lecturer, Dept. of Computer Science & Engineering, International Islamic University, Chittagong, Bangladesh

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Plain Text BibTeX RIS

APA Style

Mohammed Mahmudur Rahman. (2013). Contextual Recommender Systems Using a Multidimensional Approach. International Journal of Intelligent Information Systems, 2(4), 55-63. https://doi.org/10.11648/j.ijiis.20130204.11

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ACS Style

Mohammed Mahmudur Rahman. Contextual Recommender Systems Using a Multidimensional Approach. Int. J. Intell. Inf. Syst. 2013, 2(4), 55-63. doi: 10.11648/j.ijiis.20130204.11

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AMA Style

Mohammed Mahmudur Rahman. Contextual Recommender Systems Using a Multidimensional Approach. Int J Intell Inf Syst. 2013;2(4):55-63. doi: 10.11648/j.ijiis.20130204.11

Copy | Download

@article{10.11648/j.ijiis.20130204.11,
  author = {Mohammed Mahmudur Rahman},
  title = {Contextual Recommender Systems Using a Multidimensional Approach},
  journal = {International Journal of Intelligent Information Systems},
  volume = {2},
  number = {4},
  pages = {55-63},
  doi = {10.11648/j.ijiis.20130204.11},
  url = {https://doi.org/10.11648/j.ijiis.20130204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20130204.11},
  abstract = {Recommender systems use the past experiences and preferences of the target users as a basis to provide personalized recommendations for them and as the same time, solve the information overloading problem. Context as the dynamic information describing the situation of items and users and affecting the user’s decision process is essential to be used by recommender systems. Multidimensional approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, profiling information, and hierarchical aggregation of recommendation. The recommender system could simultaneously possess the advantages of content-based recommendation, knowledge-based recommendation, collaborative filtering recommendation and On-Line Analytical Processing (OLAP) in segmenting the information. Following the improvement of the recommendation structure, it doesn’t have to limit its analysis on the user and product to compute for the recommendation result and it could also handle and determine more complex contextual information as recommendation computation foundation. It could develop better results if applied in different domains. This work extends the multidimensional recommendation model concept of Adomavicius and Tuzhilin (2001) and proposes a multidimensional recommendation environment to integrate the contextual information.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Contextual Recommender Systems Using a Multidimensional Approach
AU  - Mohammed Mahmudur Rahman
Y1  - 2013/08/20
PY  - 2013
N1  - https://doi.org/10.11648/j.ijiis.20130204.11
DO  - 10.11648/j.ijiis.20130204.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 55
EP  - 63
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20130204.11
AB  - Recommender systems use the past experiences and preferences of the target users as a basis to provide personalized recommendations for them and as the same time, solve the information overloading problem. Context as the dynamic information describing the situation of items and users and affecting the user’s decision process is essential to be used by recommender systems. Multidimensional approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, profiling information, and hierarchical aggregation of recommendation. The recommender system could simultaneously possess the advantages of content-based recommendation, knowledge-based recommendation, collaborative filtering recommendation and On-Line Analytical Processing (OLAP) in segmenting the information. Following the improvement of the recommendation structure, it doesn’t have to limit its analysis on the user and product to compute for the recommendation result and it could also handle and determine more complex contextual information as recommendation computation foundation. It could develop better results if applied in different domains. This work extends the multidimensional recommendation model concept of Adomavicius and Tuzhilin (2001) and proposes a multidimensional recommendation environment to integrate the contextual information.
VL  - 2
IS  - 4
ER  -

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