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 Accepted: 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), 2024. Published by Science Publishing Group

Keywords

Aggregation, Contextual, Multidimensional, Recommendation

References

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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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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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