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Contextual Recommender Systems Using a Multidimensional Approach

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
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[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.
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[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.
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Cite This Article
  • 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

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

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