Development of Recommendation System for a Leasehold House for Students with Conditional Search and Reducts Rule in Rough Set

Yumoto, Masaki

doi:10.1541/ieejeiss.133.740

“…6,16 In that method, it is shown that combinations of alternatives (below referred to as representative samples) prepared as samples to design as accurate decision rules as possible must meet the following three conditions. 6,16 In that method, it is shown that combinations of alternatives (below referred to as representative samples) prepared as samples to design as accurate decision rules as possible must meet the following three conditions.…”

Section: Sample Configurationmentioning

confidence: 99%

“…1,2 Product recommendation systems are a technique to recommend products based on customer preferences, thus, drawing attention in terms of one-to-one marketing on the internet. 5,6 In these methods, similarity calculated from product attribute data is used for recommendation. 4 Information filtering is a typical technology used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

“…When developing a product recommendation system, introduction of decision support techniques is helpful for product selection. 14,16 Decision rules for rough set are generated from user's "Good" or "Bad" judgments for several samples. An efficient decision support method suitable for such product selection systems is AHP, one of the best methods for decision making and prioritization in public institutions and private businesses.…”

Section: Introductionmentioning

confidence: 99%

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Development of Decision Support System for Product Selection based on AHP, using the Decision Rule of Rough Set for Qualitative Evaluation

Yumoto

¹

2019

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The analytic hierarchy process (AHP) is an effective method for product selection support because it evaluates alternatives based on the total weight, which is a quantitative value. The total weight is obtained by multiplying the ratio of each evaluation criterion for the target user and the weights of alternatives in each evaluation criterion.The determination of weights in the qualitative evaluation criterion is difficult because of the necessity to compare the questionnaire results of all alternatives. This article proposes Decision Support System for Product Selection based on AHP, using the decision rule of Rough Set for Qualitative Evaluation. The proposed system makes decision rules based on the target user's judgment of "Good" or "Bad" on several samples in the qualitative evaluation criterion. Decision rules have a qualitative and quantitative evaluation value for each attribute, and the system calculates the weight of the AHP through normalization of these evaluation values. The user can know the results easily because the evaluation method of alternative samples in the proposed system is the same as that of correspondence with the shop assistant in an actual store. Based on two experiments, we confirm that the setup of attributes for decision rules of rough sets is one of the most important elements of the proposed system. K E Y W O R D Sanalytic hierarchy process, alternative, decision rule, decision support system for product selection, qualitative evaluation, rough conversion Electron Comm Jpn. 2019;102:15-29.

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“…However, much information about user's preferences must be collected, while changes in preferences are difficult to respond. As regards product recommendation without using user's information, some methods were proposed to recommend products based on results of neighbor search and clustering applied to product information . In these methods, similarity calculated from product attribute data is used for recommendation.…”

Section: Introductionmentioning

confidence: 99%

Development of decision support system for product selection based on AHP, using the decision rule of rough set for qualitative evaluation

Yumoto

¹

2019

Elect Comm in Japan

Self Cite

View full text Add to dashboard Cite

The analytic hierarchy process (AHP) is an effective method for product selection support because it evaluates alternatives based on the total weight, which is a quantitative value. The total weight is obtained by multiplying the ratio of each evaluation criterion for the target user and the weights of alternatives in each evaluation criterion.The determination of weights in the qualitative evaluation criterion is difficult because of the necessity to compare the questionnaire results of all alternatives. This article proposes Decision Support System for Product Selection based on AHP, using the decision rule of Rough Set for Qualitative Evaluation. The proposed system makes decision rules based on the target user's judgment of "Good" or "Bad" on several samples in the qualitative evaluation criterion. Decision rules have a qualitative and quantitative evaluation value for each attribute, and the system calculates the weight of the AHP through normalization of these evaluation values. The user can know the results easily because the evaluation method of alternative samples in the proposed system is the same as that of correspondence with the shop assistant in an actual store. Based on two experiments, we confirm that the setup of attributes for decision rules of rough sets is one of the most important elements of the proposed system. K E Y W O R D Sanalytic hierarchy process, alternative, decision rule, decision support system for product selection, qualitative evaluation, rough conversion Electron Comm Jpn. 2019;102:15-29.

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“…One method for product recommendation that does not use user information involves subjecting product information to nearest neighbor searching and/or clustering and recommending products on the basis of the processing results. 5,6 In such methods, recommendations are made by calculating similarity from the attribute information of the product. However, these methods are limited to generating general recommendations because sufficient user preferences are not understood.…”

Section: Introductionmentioning

confidence: 99%

Development of Product Recommendation System with AHP according to alternatives' evaluation with Normalization allocation and Hough conversion

Yumoto

¹

2018

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Analytic hierarchy process (AHP) is an effective method for product recommendation, because each evaluation criteria are evaluated for target objective, and each alternative are evaluated for each evaluation criteria. This method recommends alternatives to the user according to the total degrees of weight, which is calculated from these 2 kinds of evaluation values. The determination of weight is hard work, because of many evaluations. This paper proposes a product recommendation system with AHP according to Normalization allocation and Hough conversion in order to evaluate alternatives.Normalization allocation can calculate automatically the evaluation value of the alternatives for every criterion. Hough conversion can extract the number of straight line elements from the target image. Through the experiment, it is confirm that the proposal system can recommend alternatives as well as the conventional system. K E Y W O R D S AHP (Analytic Hierarchy Process), alternatives, Hough conversion, normalization allocation, product recommendation Electron Comm Jpn. 2019;102:25-41.

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Development of Recommendation System for a Leasehold House for Students with Conditional Search and Reducts Rule in Rough Set

Cited by 5 publications

References 3 publications

Development of Decision Support System for Product Selection based on AHP, using the Decision Rule of Rough Set for Qualitative Evaluation

Development of Decision Support System for Product Selection based on AHP, using the Decision Rule of Rough Set for Qualitative Evaluation

Development of decision support system for product selection based on AHP, using the decision rule of rough set for qualitative evaluation

Development of Product Recommendation System with AHP according to alternatives' evaluation with Normalization allocation and Hough conversion

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