Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve
The recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users’ score data and interest’s shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/10/4/130 |
| _version_ | 1819029326590377984 |
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| author | Taoying Li Linlin Jin Zebin Wu Yan Chen |
| author_facet | Taoying Li Linlin Jin Zebin Wu Yan Chen |
| author_sort | Taoying Li |
| collection | DOAJ |
| description | The recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users’ score data and interest’s shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting curve is proposed. Firstly, the Pearson similarity is improved by a wide range of weighted factors to enhance the quality of Pearson similarity for high sparse data. Secondly, the Ebbinghaus forgetting curve is introduced to track a user’s interest shift. User score is weighted according to the residual memory of forgetting function. Users’ interest changing with time is tracked by scoring, which increases both accuracy of recommendation algorithm and users’ satisfaction. The two algorithms are then combined together. Finally, the MovieLens dataset is employed to evaluate different algorithms and results show that the proposed algorithm decreases mean absolute error (MAE) by 12.2%, average coverage 1.41%, and increases average precision by 10.52%, respectively. |
| first_indexed | 2024-12-21T06:12:29Z |
| format | Article |
| id | doaj.art-f46e09e6aeb346378ea7d7c97f63ff69 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-12-21T06:12:29Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-f46e09e6aeb346378ea7d7c97f63ff692022-12-21T19:13:30ZengMDPI AGInformation2078-24892019-04-0110413010.3390/info10040130info10040130Combined Recommendation Algorithm Based on Improved Similarity and Forgetting CurveTaoying Li0Linlin Jin1Zebin Wu2Yan Chen3School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaThe recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users’ score data and interest’s shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting curve is proposed. Firstly, the Pearson similarity is improved by a wide range of weighted factors to enhance the quality of Pearson similarity for high sparse data. Secondly, the Ebbinghaus forgetting curve is introduced to track a user’s interest shift. User score is weighted according to the residual memory of forgetting function. Users’ interest changing with time is tracked by scoring, which increases both accuracy of recommendation algorithm and users’ satisfaction. The two algorithms are then combined together. Finally, the MovieLens dataset is employed to evaluate different algorithms and results show that the proposed algorithm decreases mean absolute error (MAE) by 12.2%, average coverage 1.41%, and increases average precision by 10.52%, respectively.https://www.mdpi.com/2078-2489/10/4/130forgetting curvecombined recommendationcollaborative filtersimilarity degree |
| spellingShingle | Taoying Li Linlin Jin Zebin Wu Yan Chen Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve Information forgetting curve combined recommendation collaborative filter similarity degree |
| title | Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve |
| title_full | Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve |
| title_fullStr | Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve |
| title_full_unstemmed | Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve |
| title_short | Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve |
| title_sort | combined recommendation algorithm based on improved similarity and forgetting curve |
| topic | forgetting curve combined recommendation collaborative filter similarity degree |
| url | https://www.mdpi.com/2078-2489/10/4/130 |
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