COMER: ClOud-based MEdicine Recommendation
With the development of e-commerce, a growing number of people prefer to purchase medicine online for the sake of convenience.However, it is a serious issue to purchase medicine blindly without necessary medication guidance.In this paper, we propose a novel cloud-based medicine recommendation, which...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
European Alliance for Innovation (EAI)
2016-12-01
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| Series: | EAI Endorsed Transactions on Cloud Systems |
| Subjects: | |
| Online Access: | https://eudl.eu/pdf/10.4108/icst.qshine.2014.256542 |
| _version_ | 1818194362784284672 |
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| author | Yin Zhang Long Wang Long Hu Xiaofei Wang Min Chen |
| author_facet | Yin Zhang Long Wang Long Hu Xiaofei Wang Min Chen |
| author_sort | Yin Zhang |
| collection | DOAJ |
| description | With the development of e-commerce, a growing number of people prefer to purchase medicine online for the sake of convenience.However, it is a serious issue to purchase medicine blindly without necessary medication guidance.In this paper, we propose a novel cloud-based medicine recommendation, which can recommend users with top-N related medicines according to symptoms.Firstly, we cluster the drugs into several groups according to the functional description information, and design a basic personalized medicine recommendation based on user collaborative filtering.Then, considering the shortcomings of collaborative filtering algorithm, such as computing expensive, cold start, and data sparsity, we propose a cloud-based approach for enriching end-user Quality of Experience (QoE) of medicine recommendation, by modeling and representing the relationship of the user, symptom and medicine via tensor decomposition.Finally, the proposed approach is evaluated with experimental study based on a real dataset crawled from Internet. |
| first_indexed | 2024-12-12T01:01:05Z |
| format | Article |
| id | doaj.art-509e0a3f5805446ca6adbe082dbe6390 |
| institution | Directory Open Access Journal |
| issn | 2410-6895 |
| language | English |
| last_indexed | 2024-12-12T01:01:05Z |
| publishDate | 2016-12-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Cloud Systems |
| spelling | doaj.art-509e0a3f5805446ca6adbe082dbe63902022-12-22T00:43:44ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Cloud Systems2410-68952016-12-012710.4108/icst.qshine.2014.256542COMER: ClOud-based MEdicine RecommendationYin Zhang0Long Wang1Long Hu2Xiaofei Wang3Min Chen4Departent of Computer Science and Technology, Huazhong University of Science and TechnologyDepartent of Computer Science and Technology, Huazhong University of Science and TechnologyDepartent of Computer Science and Technology, Huazhong University of Science and TechnologyDepartment of Electrical and Computer Engineering, The University of British ColumbiaDepartent of Computer Science and Technology, Huazhong University of Science and TechnologyWith the development of e-commerce, a growing number of people prefer to purchase medicine online for the sake of convenience.However, it is a serious issue to purchase medicine blindly without necessary medication guidance.In this paper, we propose a novel cloud-based medicine recommendation, which can recommend users with top-N related medicines according to symptoms.Firstly, we cluster the drugs into several groups according to the functional description information, and design a basic personalized medicine recommendation based on user collaborative filtering.Then, considering the shortcomings of collaborative filtering algorithm, such as computing expensive, cold start, and data sparsity, we propose a cloud-based approach for enriching end-user Quality of Experience (QoE) of medicine recommendation, by modeling and representing the relationship of the user, symptom and medicine via tensor decomposition.Finally, the proposed approach is evaluated with experimental study based on a real dataset crawled from Internet.https://eudl.eu/pdf/10.4108/icst.qshine.2014.256542cloudqoemedicine recommendationcollaborative filteringclusteringtensor decomposition |
| spellingShingle | Yin Zhang Long Wang Long Hu Xiaofei Wang Min Chen COMER: ClOud-based MEdicine Recommendation EAI Endorsed Transactions on Cloud Systems cloud qoe medicine recommendation collaborative filtering clustering tensor decomposition |
| title | COMER: ClOud-based MEdicine Recommendation |
| title_full | COMER: ClOud-based MEdicine Recommendation |
| title_fullStr | COMER: ClOud-based MEdicine Recommendation |
| title_full_unstemmed | COMER: ClOud-based MEdicine Recommendation |
| title_short | COMER: ClOud-based MEdicine Recommendation |
| title_sort | comer cloud based medicine recommendation |
| topic | cloud qoe medicine recommendation collaborative filtering clustering tensor decomposition |
| url | https://eudl.eu/pdf/10.4108/icst.qshine.2014.256542 |
| work_keys_str_mv | AT yinzhang comercloudbasedmedicinerecommendation AT longwang comercloudbasedmedicinerecommendation AT longhu comercloudbasedmedicinerecommendation AT xiaofeiwang comercloudbasedmedicinerecommendation AT minchen comercloudbasedmedicinerecommendation |