Based on linguistic evaluations, a linguistic threeway decision method is proposed. First, the alternatives are rated in linguistic forms and divided into acceptance, rejection and uncertainty regions. Secondly, the l...Based on linguistic evaluations, a linguistic threeway decision method is proposed. First, the alternatives are rated in linguistic forms and divided into acceptance, rejection and uncertainty regions. Secondly, the linguistic three-way group decision steps are provided. Specifically, the experts determine the lower bound and upper bound of the uncertainty region, respectively. When the evaluation is superior to the upper bound, the corresponding alternative is put into the acceptance region directly. Similarly, when the evaluation is inferior to the lower bound, the corresponding alternative is put into the rejection region directly, and the remaining alternatives are put into the uncertain region. Moreover, the objects in the uncertainty region are especially discussed. The linguistic terms are transformed into fuzzy numbers and then aggregated. Finally, a recommendation example is provided to illustrate the practicality and validity of the proposed method.展开更多
A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared...A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.展开更多
With time going on, the fact that pace of life becomes faster make more and more customers pay more attention to of clothing. In order to survive and develop better and to attract more customers, enterprisesmust have ...With time going on, the fact that pace of life becomes faster make more and more customers pay more attention to of clothing. In order to survive and develop better and to attract more customers, enterprisesmust have the ability to provide the personalized recommendations and the implementation of differentiated business strategy. This text aims to make enterprises understand the customers＇ personalized requirement by using the data processed though questionnaire and rough set theory. And enterprises can provide production and marketing auxiliary decision-making effectively. The feasibility and practicality of rough set theory is verified through the personalized recommendationseases.展开更多
基金The National Natural Science Foundation of China(No.71171048,71371049)the Scientific Innovation Research of College Graduates in Jiangsu Province(No.KYLX15-0190)the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1567)
文摘Based on linguistic evaluations, a linguistic threeway decision method is proposed. First, the alternatives are rated in linguistic forms and divided into acceptance, rejection and uncertainty regions. Secondly, the linguistic three-way group decision steps are provided. Specifically, the experts determine the lower bound and upper bound of the uncertainty region, respectively. When the evaluation is superior to the upper bound, the corresponding alternative is put into the acceptance region directly. Similarly, when the evaluation is inferior to the lower bound, the corresponding alternative is put into the rejection region directly, and the remaining alternatives are put into the uncertain region. Moreover, the objects in the uncertainty region are especially discussed. The linguistic terms are transformed into fuzzy numbers and then aggregated. Finally, a recommendation example is provided to illustrate the practicality and validity of the proposed method.
基金supporting by grant fund under the Strategic Scholarships for Frontier Research Network for the PhD Program Thai Doctoral degree
文摘A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.
基金This work is supported by the National Natural Science Foundation of China （No. 71301100）, Innovation Program of Shanghai Municipal Education Commission（No. 14YZ140 and No. ZZGJD12036）, Innovation Program of ShanghaiUniversity of Engineering Science （NO. E1-0903-15-01143, Title： 15KY0354Research on personalizedrecommendafionof clothing based on Data Mining） and Doctorate Foundation of Shanghai（No. 11692191400）.
文摘With time going on, the fact that pace of life becomes faster make more and more customers pay more attention to of clothing. In order to survive and develop better and to attract more customers, enterprisesmust have the ability to provide the personalized recommendations and the implementation of differentiated business strategy. This text aims to make enterprises understand the customers＇ personalized requirement by using the data processed though questionnaire and rough set theory. And enterprises can provide production and marketing auxiliary decision-making effectively. The feasibility and practicality of rough set theory is verified through the personalized recommendationseases.
