Satoshi Takahashi

Yuta Sumiya, Ryusei Numata, Satoshi Takahashi

Recently, electronic commerce (EC) websites have been unable to provide an identification number (user ID) for each transaction data entry because of privacy issues. Because most recommendation methods assume that all data are assigned a user ID, they cannot be applied to the data without user IDs. Recently, session-based recommendation (SBR) based on session information, which is short-term behavioral information of users, has been studied. A general SBR uses only information about the item of interest to make a recommendation (e.g., item ID for an EC site). Particularly in the case of EC sites, the data recorded include the name of the item being purchased, the price of the item, the category hierarchy, and the gender and region of the user. In this study, we define a pseudo--session for the purchase history data of an EC site without user IDs and session IDs. Finally, we propose an SBR with a co-guided heterogeneous hypergraph and globalgraph network plus, called CoHHGN+. The results show that our CoHHGN+ can recommend items with higher performance than other methods.

Satoshi Takahashi, Keiko Yamaguchi, Asuka Watanabe

The analysis of daily-life fashion trends can provide us a profound understanding of our societies and cultures. However, no appropriate digital archive exists that includes images illustrating what people wore in their daily lives over an extended period. In this study, we propose a new fashion image archive, Chronicle Archive of Tokyo Street-fashion (CAT STREET), to shed light on daily-life fashion trends. CAT STREET includes images showing what people wore in their daily lives during 1970--2017, and these images contain timestamps and street location annotations. This novel database combined with machine learning enables us to observe daily-life fashion trends over a long term and analyze them quantitatively. To evaluate the potential of our proposed approach with the novel database, we corroborated the rules-of-thumb of two fashion trend phenomena that have been observed and discussed qualitatively in previous studies. Through these empirical analyses, we verified that our approach to quantify fashion trends can help in exploring unsolved research questions. We also demonstrate CAT STREET's potential to find new standpoints to promote the understanding of societies and cultures through fashion embedded in consumers' daily lives.