Research Paper:
Dynamic Short-Term Perspective Estimation Based on Formal Concept Analysis
Kazuki Aikawa and Hajime Nobuhara
Department of Intelligent Interaction Technologies, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Corresponding author
In online shopping, user perspectives transit dynamically from abstract categories to concrete subcategories within a short period. We propose a perspective-estimation system that estimates the dynamic, short-term perspectives of users by inferring a hierarchy of categories based on their actions. The proposed system analyzes the wish list rankings of users and their operational histories to extract the categories emphasized at that moment. It then employs formal concept analysis to infer the hierarchical structure of categories, thereby visualizing the dynamic short-term perspective. In experiments involving 57 participants, the proposed method rates its match with user perspectives on a seven-point scale, achieving an average score of 4.84, outperforming the feature estimation method using latent Dirichlet allocation (LDA), which scored 4.36. The statistical significance was confirmed through the Wilcoxon rank-sum test with a statistic W=4.80 and a p-value of 1.56×10-6. Compared with LDA, the proposed system is statistically significant in terms of the degree of agreement with the perspectives.
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