[1A4-GS-2] Machine learning: recommendation / feature analysis

[1A4-GS-2-04] A Study on Item Analysis Method Considering the Persistence of User Interest Based on Hidden Semi-Markov Model

〇Kirin Tsuchiya¹, Yuki Tsuboi¹, Ryotaro Shimizu¹, Goto Masayuki¹ (1. Waseda University)

Keywords:Time Series Model, Markov Model, Hidden Semi-Markov Model, Item Analysis, Persistence of User Interest

The competition for customers among video distribution services is intensifying. In general, the user's purchasing actions for video contents (items), unlike daily necessities, have a strong influence on the their real-time interests during viewing items (consumption). In other words, the user's interest after the consumption of an item is determined by the influence of the previous item for the user's interest persistence (interest persistence probability under the item). Therefore, it is important to select and evaluate items based on the interest persistence probability under the item in order to have users use the service for a long time is important. Hidden Semi-Markov Models (HSMM) was proposed as a model for predicting the next item to be consumed by a user while taking into account the user's interest persistence. If the interest persistence probability under an item can be calculated and analyzed using HSMM, the new insights leading to the marketing strategies can be expected. In this study, we propose an analysis process using item clustering based on the distribution of the interest persistence probabilities under the items, utilizing the characteristics of HSMM. In addition, we show the effectiveness of our proposed method by applying the actual data set.