Finding various factors hidden in data

Our research focuses on finding various factors hidden in data. Suppose that we have a dataset of purchase records in which each purchase is represented by hashed user ID, user attribute, purchased good, purchase time, and store. Our method analyses the dataset and finds some specific tendencies, e.g., business persons tend to buy burgers for lunch at convenience stores, in an efficient way and in a precise manner. For efficient computations, we optimize the data structure and algorithm so that sparse data entries are aligned and accessed sequentially. For preciseness, we introduce some constraints that represents the relationship between a user ID and its attribute. Our proposed method contributes to discovering new insights from ever increasing behavior logs of humans and machines.

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