Time-Aware Explainable Recommendation via Updating Enabled Online Prediction
There has been growing attention on explainable recommendation that is able to provide high-quality results as well as intuitive explanations. However, most existing studies use offline prediction strategies where recommender systems are trained once while used forever, which ignores the dynamic and...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/24/11/1639 |
| _version_ | 1797465380012163072 |
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| author | Tianming Jiang Jiangfeng Zeng |
| author_facet | Tianming Jiang Jiangfeng Zeng |
| author_sort | Tianming Jiang |
| collection | DOAJ |
| description | There has been growing attention on explainable recommendation that is able to provide high-quality results as well as intuitive explanations. However, most existing studies use offline prediction strategies where recommender systems are trained once while used forever, which ignores the dynamic and evolving nature of user–item interactions. There are two main issues with these methods. First, their random dataset split setting will result in data leakage that knowledge should not be known at the time of training is utilized. Second, the dynamic characteristics of user preferences are overlooked, resulting in a model aging issue where the model’s performance degrades along with time. In this paper, we propose an updating enabled online prediction framework for the time-aware explainable recommendation. Specifically, we propose an online prediction scheme to eliminate the data leakage issue and two novel updating strategies to relieve the model aging issue. Moreover, we conduct extensive experiments on four real-world datasets to evaluate the effectiveness of our proposed methods. Compared with the state-of-the-art, our time-aware approach achieves higher accuracy results and more convincing explanations for the entire lifetime of recommendation systems, i.e., both the initial period and the long-term usage. |
| first_indexed | 2024-03-09T18:20:42Z |
| format | Article |
| id | doaj.art-82476ea64d7441149e8bb973175885dc |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T18:20:42Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-82476ea64d7441149e8bb973175885dc2023-11-24T08:18:26ZengMDPI AGEntropy1099-43002022-11-012411163910.3390/e24111639Time-Aware Explainable Recommendation via Updating Enabled Online PredictionTianming Jiang0Jiangfeng Zeng1School of Information Management, Central China Normal University, Wuhan 430079, ChinaSchool of Information Management, Central China Normal University, Wuhan 430079, ChinaThere has been growing attention on explainable recommendation that is able to provide high-quality results as well as intuitive explanations. However, most existing studies use offline prediction strategies where recommender systems are trained once while used forever, which ignores the dynamic and evolving nature of user–item interactions. There are two main issues with these methods. First, their random dataset split setting will result in data leakage that knowledge should not be known at the time of training is utilized. Second, the dynamic characteristics of user preferences are overlooked, resulting in a model aging issue where the model’s performance degrades along with time. In this paper, we propose an updating enabled online prediction framework for the time-aware explainable recommendation. Specifically, we propose an online prediction scheme to eliminate the data leakage issue and two novel updating strategies to relieve the model aging issue. Moreover, we conduct extensive experiments on four real-world datasets to evaluate the effectiveness of our proposed methods. Compared with the state-of-the-art, our time-aware approach achieves higher accuracy results and more convincing explanations for the entire lifetime of recommendation systems, i.e., both the initial period and the long-term usage.https://www.mdpi.com/1099-4300/24/11/1639explainable recommendationdata leakagemodel agingonline predictionmodel updating |
| spellingShingle | Tianming Jiang Jiangfeng Zeng Time-Aware Explainable Recommendation via Updating Enabled Online Prediction Entropy explainable recommendation data leakage model aging online prediction model updating |
| title | Time-Aware Explainable Recommendation via Updating Enabled Online Prediction |
| title_full | Time-Aware Explainable Recommendation via Updating Enabled Online Prediction |
| title_fullStr | Time-Aware Explainable Recommendation via Updating Enabled Online Prediction |
| title_full_unstemmed | Time-Aware Explainable Recommendation via Updating Enabled Online Prediction |
| title_short | Time-Aware Explainable Recommendation via Updating Enabled Online Prediction |
| title_sort | time aware explainable recommendation via updating enabled online prediction |
| topic | explainable recommendation data leakage model aging online prediction model updating |
| url | https://www.mdpi.com/1099-4300/24/11/1639 |
| work_keys_str_mv | AT tianmingjiang timeawareexplainablerecommendationviaupdatingenabledonlineprediction AT jiangfengzeng timeawareexplainablerecommendationviaupdatingenabledonlineprediction |