TY - GEN
T1 - Data Completeness Impact on Deep Learning Based Explainable Recommender Systems
AU - Hakim, Deni Lukmanul
AU - Darari, Fariz
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Recommendation systems become an important part in helping users find the most fit items in many domains, such as healthcare, transportation, agriculture, media, and e-commerce. The development of an explainable recommendation system was claimed to add more value to improve user satisfaction. Various studies have been conducted regarding the model to solve real world issues. Yet, the impact of data quality in building an explainable recommendation system is still scarce. In this paper, we investigate whether the completeness of data used in building explainable recommendation system will impact the performance of the recommender systems and quality of the explanation. We use the Yelp and MovieLens dataset and train a deep learning explainable recommendation system model, Co-Attentive Multi-task Learning (CAML), on various amounts of data by reducing the data records using algorithm technique to achieve Missing Completely at Random (MCAR) and Missing at Random (MAR), and by eliminating selected features on each dataset. Finally, we evaluated the outcomes based on Root Mean Square Error (RMSE) for rating evaluation and Bilingual Evaluation Understudy (BLEU) & Recall-Oriented Understudy for Gisting Evaluation (ROUGE) for explanation evaluation. Our experiments conclude with Pearson Correlation Coefficient between completeness of data with the evaluation result in each of the experiment. We found out that different types of data reduction and dataset impact differently to the level of the performance of the ratings and explanation.
AB - Recommendation systems become an important part in helping users find the most fit items in many domains, such as healthcare, transportation, agriculture, media, and e-commerce. The development of an explainable recommendation system was claimed to add more value to improve user satisfaction. Various studies have been conducted regarding the model to solve real world issues. Yet, the impact of data quality in building an explainable recommendation system is still scarce. In this paper, we investigate whether the completeness of data used in building explainable recommendation system will impact the performance of the recommender systems and quality of the explanation. We use the Yelp and MovieLens dataset and train a deep learning explainable recommendation system model, Co-Attentive Multi-task Learning (CAML), on various amounts of data by reducing the data records using algorithm technique to achieve Missing Completely at Random (MCAR) and Missing at Random (MAR), and by eliminating selected features on each dataset. Finally, we evaluated the outcomes based on Root Mean Square Error (RMSE) for rating evaluation and Bilingual Evaluation Understudy (BLEU) & Recall-Oriented Understudy for Gisting Evaluation (ROUGE) for explanation evaluation. Our experiments conclude with Pearson Correlation Coefficient between completeness of data with the evaluation result in each of the experiment. We found out that different types of data reduction and dataset impact differently to the level of the performance of the ratings and explanation.
KW - Completeness
KW - Data quality
KW - Explainable recommender systems
KW - Explanation
KW - Rating prediction
UR - http://www.scopus.com/inward/record.url?scp=85125955021&partnerID=8YFLogxK
U2 - 10.1109/ICOIACT53268.2021.9563919
DO - 10.1109/ICOIACT53268.2021.9563919
M3 - Conference contribution
AN - SCOPUS:85125955021
T3 - ICOIACT 2021 - 4th International Conference on Information and Communications Technology: The Role of AI in Health and Social Revolution in Turbulence Era
SP - 262
EP - 267
BT - ICOIACT 2021 - 4th International Conference on Information and Communications Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Information and Communications Technology, ICOIACT 2021
Y2 - 30 August 2021
ER -