Jong-Bum Baik

An automatic classification system for consumer regulatory focus by analyzing web shopping logs

According to regulatory focus theory, a representative theory on consumer behavior, human personality can be divided into two types: promotion and prevention. These two personality types have much influence on the consumers decision in many diverse areas, such as information exploration, information processing, and the evaluation of alternatives. In this research, we try to classify the consumers regulatory focus using web shopping logs as the groundwork for adapting it to personalized recommendation. For this purpose, we define the consumers behavior variables, utilitarian preference index, and information exploration activity index by analyzing the web shopping logs. We then use these variables as inputs to learn a classifier for predicting the consumers regulatory focus. This research shows the possibility of systematization of the consumer behavior theory as an interdisciplinary research of social science and information technology. Based on this attempt, research can be extended to IT services adapting social science theories to a variety of areas, apart from the consumer behavior area.

Predicting personality traits related to consumer behavior using SNS analysis

ABSTRACT Modeling a user profile is one of the important factors for devising a personalized recommendation. The traditional approach for modeling a user profile in computer science is to collect and generalize the users buying behavior or preference history, generated from the users interactions with recommender systems. According to consumer behavior research, however, internal factors such as personality traits influence a consumers buying behavior. Existing studies have tried to adapt the Big 5 personality traits to personalized recommendations. However, although studies have shown that these traits can be useful to some extent for personalized recommendation, the causal relationship between the Big 5 personality traits and the buying behaviors of actual consumers has not been validated. In this paper, we propose a novel method for predicting the four personality traits—Extroversion, Public Self-consciousness, Desire for Uniqueness, and Self-esteem—that correlate with buying behaviors. The proposed method automatically constructs a user-personality-traits prediction model for each user by analyzing the user behavior on a social networking service. The experimental results from an analysis of the collected Facebook data show that the proposed method can predict user-personality traits with greater precision than methods that use the variables proposed in previous studies.

Regulatory Focus Classification for Web Shopping Consumers According to Product Type

소비자 행동이론에 따르면 사람의 성향은 향상초점과 예방초점이라는 두 가지 조절초점 유형으로 나누어지며, 이 두 가지 성향은 다양한 영 역에 있어서 소비자의 의사결정에 많은 영향을 미치는 것으로 알려져 있다. 본 연구에서는 개인화 추천에서 Cold Start 문제의 최소화 및 추천 알고리즘 성능 개선을 위하여 조절초점이론을 적용한다. 이를 위하여, 웹쇼핑 로그로부터 소비자 별 행동변수, 정보탐색활동성 지수를 추출하고 이를 활용한 소비자 조절초점성향 분류 방법을 제안한다. 본 연구는 사회과학/IT 융합 연구로서 소비자행동 이론의 시스템화 가능성을 입증하 였다는 점에 있어서 의의를 지니며, 향후 다양한 분야의 이론들을 적용한 IT 서비스에 대한 연구로 확장하고자 한다.

A Study on Spam Document Classification Method using Characteristics of Keyword Repetition

In Web environment, a flood of spam causes serious social problems such as personal information leak, monetary loss from fishing and distribution of harmful contents. Moreover, types and techniques of spam distribution which must be controlled are varying as days go by. The learning based spam classification method using Bag-of-Words model is the most widely used method until now. However, this method is vulnerable to anti-spam avoidance techniques, which recent spams commonly have, because it classifies spam documents utilizing only keyword occurrence information from classification model training process. In this paper, we propose a spam document detection method using a characteristic of repeating words occurring in spam documents as a solution of anti-spam avoidance techniques. Recently, most spam documents have a trend of repeating key phrases that are designed to spread, and this trend can be used as a measure in classifying spam documents. In this paper, we define six variables, which represent a characteristic of word repetition, and use those variables as a feature set for constructing a classification model. The effectiveness of proposed method is evaluated by an experiment with blog posts and E-mail data. The result of experiment shows that the proposed method outperforms other approaches.