Similarity Measure for Product Attribute Estimation

Representing products as a combination of properties that capture the essence of consumer sentiment is critical for companies that strive to understand consumer behavior. A catalogue of products described in terms of their attributes could offer companies a wide range of benefits; from improving existing products or developing new ones, to improving the quality of site search and offering better item recommendations to users. In this paper, we propose a method that encodes products as a sequence of attributes, each of which represents a different dimension of the consumer perception. In the proposed method, first, a base product set with known attribute values is built based on consumers' perceptions. Then, new product attribute vectors are estimated using product similarity. The proposed method also incorporates a new similarity measure that is based on purchase behavior and which is suitable for estimating product attribute vector distances. Because it takes into account the magnitude of the individual components of the vectors under comparison, the proposed method is free from the limitations of conventional similarity measures. The results of experiments conducted using real-world data indicate that the proposed method has superior performance compared to conventional approaches in terms of mean absolute error (MAE) and root mean squared error (RMSE).