Finding conferences with papers relevant to their interests can be difficult for research conference attendees because everyone has different preferences. To address this issue, this research proposes a framework and a prototype of a personalized recommendation system for research conference items. When making recommendations, the prototype considers the user's research area and college. The prototype employs three algorithms to recommend conference papers based on what users have previously read: a collaborative filtering algorithm (k-Nearest Neighbor), a content-based filtering algorithm, and a hybrid of the two. The design science research paradigm was used to write the research. This research covers the conceptual framework design and prototype implementation in programming languages that the researcher is capable of implementing, as well as a brief state of the art of the recommending systems literature. The prototype's usability was assessed using the information retrieval concept. To assess the quality of recommendations, system performance and a user-centered evaluation were performed. The usability evaluation results showed that users were generally pleased with the prototype's usability. Users who tested the prototype were generally pleased with the quality of the recommendations. The performance of a prototype system is 86 percent, and user acceptance is 86.5 percent. Finally, future works in the area are clearly stated.
| Published in | International Journal of Data Science and Analysis (Volume 8, Issue 5) |
| DOI | 10.11648/j.ijdsa.20220805.11 |
| Page(s) | 131-148 |
| 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), 2022. Published by Science Publishing Group |
Research Conference, Mobile Based Systems, SMO Classifier, Framework, Android, Recommender System
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APA Style
Aklilu Mandefro Messele. (2022). A Framework for Mobile Based Research Paper Recommendation in a Conference. International Journal of Data Science and Analysis, 8(5), 131-148. https://doi.org/10.11648/j.ijdsa.20220805.11
ACS Style
Aklilu Mandefro Messele. A Framework for Mobile Based Research Paper Recommendation in a Conference. Int. J. Data Sci. Anal. 2022, 8(5), 131-148. doi: 10.11648/j.ijdsa.20220805.11
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Download@article{10.11648/j.ijdsa.20220805.11,
author = {Aklilu Mandefro Messele},
title = {A Framework for Mobile Based Research Paper Recommendation in a Conference},
journal = {International Journal of Data Science and Analysis},
volume = {8},
number = {5},
pages = {131-148},
doi = {10.11648/j.ijdsa.20220805.11},
url = {https://doi.org/10.11648/j.ijdsa.20220805.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220805.11},
abstract = {Finding conferences with papers relevant to their interests can be difficult for research conference attendees because everyone has different preferences. To address this issue, this research proposes a framework and a prototype of a personalized recommendation system for research conference items. When making recommendations, the prototype considers the user's research area and college. The prototype employs three algorithms to recommend conference papers based on what users have previously read: a collaborative filtering algorithm (k-Nearest Neighbor), a content-based filtering algorithm, and a hybrid of the two. The design science research paradigm was used to write the research. This research covers the conceptual framework design and prototype implementation in programming languages that the researcher is capable of implementing, as well as a brief state of the art of the recommending systems literature. The prototype's usability was assessed using the information retrieval concept. To assess the quality of recommendations, system performance and a user-centered evaluation were performed. The usability evaluation results showed that users were generally pleased with the prototype's usability. Users who tested the prototype were generally pleased with the quality of the recommendations. The performance of a prototype system is 86 percent, and user acceptance is 86.5 percent. Finally, future works in the area are clearly stated.},
year = {2022}
}
TY - JOUR T1 - A Framework for Mobile Based Research Paper Recommendation in a Conference AU - Aklilu Mandefro Messele Y1 - 2022/09/29 PY - 2022 N1 - https://doi.org/10.11648/j.ijdsa.20220805.11 DO - 10.11648/j.ijdsa.20220805.11 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 131 EP - 148 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20220805.11 AB - Finding conferences with papers relevant to their interests can be difficult for research conference attendees because everyone has different preferences. To address this issue, this research proposes a framework and a prototype of a personalized recommendation system for research conference items. When making recommendations, the prototype considers the user's research area and college. The prototype employs three algorithms to recommend conference papers based on what users have previously read: a collaborative filtering algorithm (k-Nearest Neighbor), a content-based filtering algorithm, and a hybrid of the two. The design science research paradigm was used to write the research. This research covers the conceptual framework design and prototype implementation in programming languages that the researcher is capable of implementing, as well as a brief state of the art of the recommending systems literature. The prototype's usability was assessed using the information retrieval concept. To assess the quality of recommendations, system performance and a user-centered evaluation were performed. The usability evaluation results showed that users were generally pleased with the prototype's usability. Users who tested the prototype were generally pleased with the quality of the recommendations. The performance of a prototype system is 86 percent, and user acceptance is 86.5 percent. Finally, future works in the area are clearly stated. VL - 8 IS - 5 ER -
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