Rank order-based recommendation approach for multiple featured products |
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| Authors: | Sang Hyun Choi Byeong Seok Ahn |
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| Affiliation: | 1. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, China, Changchun 130012, China;2. College of Computer Science and Technology, Jilin University, Changchun, China;3. Centre for Creative Computing, Bath Spa University, Corsham SN13 0BZ, England United Kingdom;1. IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Portugal, Av. Rovisco Pais, Lisboa 049-001, Portugal;2. University of Huddersfield, Institute of Railway Research, Queensgate, Huddersfield, HD1 3DH, UK;1. School of Management, Shanghai University, Shanghai 200444, China;2. The Logistics Institute-Asia Pacific, National University of Singapore, 119613, Singapore;3. School of Management, Shenzhen Polytechnic, Shenzhen 518055, China;4. The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518054, China;1. School of Mathematical Sciences, Tongji University, Shanghai, 200000, China;2. School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, Henan, 450001, China;3. Henan Key Laboratory of Financial Engineering, Zhengzhou, Henan, 450001, China |
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| Abstract: | Recommendation methods, which suggest a set of products likely to be of interest to a customer, require a great deal of information about both the user and the products. Recommendation methods take different forms depending on the types of preferences required from the customer. In this paper, we propose a new recommendation method that attempts to suggest products by utilizing simple information, such as ordinal specification weights and specification values, from the customer. These considerations lead to an ordinal weight-based multi-attribute value model. This model is well suited to situations in which there exist insufficient data regarding the demographics and transactional information on the target customers, because it enables us to recommend personalized products with a minimal input of customer preferences. The proposed recommendation method is different from previously reported recommendation methods in that it explicitly takes into account multidimensional features of each product by employing an ordered weight-based multi-attribute value model. To evaluate the proposed method, we conduct comparative experiments with two other methods rooted in distance-based similarity measures. |
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