Furniture Recommendations Based on User Propensity and Furniture Style Compatibility

As digital information becomes more voluminous and e-commerce becomes more widespread, there is a growing demand for item recommendations that match the users' sensibilities. However, learning users' propensities is a difficult problem, especially in the field of furniture, which requires the consideration of many factors, such as color and shape. In addition, pieces of furniture should not be recommended only as stand-alone items, but must also be considered in terms of their affinity with other pieces, making the compatibility of styles among them an important factor. However, a consumer's furniture style is an ambiguous concept that is difficult to define. To reduce this ambiguity, Siamese networks are often used to estimate style compatibility by adding various features that represent styles, but even when they make use of alternative features associated with images, they are difficult to represent accurately. This paper proposes a method for recommending multiple pieces of furniture by learning style compatibility properties with a high degree of accuracy, taking users' preferences and styles' compatibility into account. To this end, we engaged in two tasks: (1) extracting users' preferences and (2) improving the accuracy of style suitability estimation. For (1), we applied matrix factorization to identify users whose sensitivities were close to those of the users who will receive recommendations. For (2), we used the Siamese network we have already proposed, which can learn from multiple furniture images simultaneously. Specifically, we propose a one-to-many input ratio to maintain high performance even when the input is ambiguous. Validation experiments were conducted for each task, and the results showed that the performance was improved; the actual recommendation results also showed a high performance.