A Latent Class Analysis for Item Demand Based on Temperature Difference and Store Characteristics

다국어 초록 (Multilingual Abstract)

In retail stores, there is an increasing need for predicting item demand using accumulated purchase history data to cope with the fluctuating consumer demands. These fluctuations in item demand are influenced by external factors and consumer preferences. Among these, store characteristics and weather conditions, which are closely related to consumer behavior, have strong effects on item demand. For this reason, it is very important to quantitatively grasp demand fluctuations of items that are influenced by changes in weather conditions for each store by using an integrated analysis of the purchase history data of many stores and weather conditions. In this research, we focus on the temperature difference, which is the average temperature difference from the previous day, as a weather condition affecting item sales. Because consumer feeling about a temperature is dependent on the temperature difference from the previous day, it is meaningful to construct a prediction model using this information. In this research, we propose a latent class model to express the relationship between weather conditions, store characteristics, and item demand fluctuation. Also, through an analysis experiment using an actual data set, we show the usefulness of the proposed model by extracting items that are influenced by weather conditions.

목차 (Table of Contents)

• ABSTRACT
• 1. INTRODUCTION
• 2. PREPARATION
• 3. PROPOSED ANALYSIS METHOD
• 4. ANALYSIS EXPERIMENT
• 5. DISCUSSION
• 6. CONCLUSION AND FUTURE WORKS
• REFERENCES

참고문헌 (Reference)

1 Xue, G. R., "Topicbridged PLSA for cross-domain text classification" 627-634, 2008

2 Iwata, T., "Topic tracking model for analyzing consumer purchase behavior" 11-17, 2009

3 Swait, J., "The influence of task complexity on consumer choice : A latent class model of decision strategy switching" 28 (28): 135-148, 2001

4 Miyakawa, M., "The EM algorithm and its related problems" 16 (16): 1-21, 1987

5 Abe, M, "Science of Marketing -Analysis of POS Data" Asakura Shoten 2005

6 Bosch, A., "Scene classification via pLSA" 517-530, 2006

7 Okayama, S., "Relational analysis model of weather conditions and sales patterns based on nonnegative matrix factorization" 58 (58): 2477-2489, 2019

8 Fujihara, S., "Quantitative sociological approaches using the latent class analysis : Data Analyses of status inconsistency, attitude to social inequality, and authoritarian-conservatism" 33 : 43-68, 2012

9 Ueda, S., "Probabilistic model of multiple topic text : The forefront of text model research" 45 (45): 282-289, 2004

10 Hofmann, T., "Probabilistic latent semantic analysis" 289-296, 1999

11 Jin, R., "Preference-based graphic models for collaborative filtering" 329-336, 2003

12 Bishop, C. M., "Pattern Recognition and Machine Learning" Springer 2006

13 Sagawa, M, "Marketing / Data Analysis" Asakura Shoten 2003

14 Hofmann, T., "Latent semantic models for collaborative filtering" 22 (22): 89-115, 2004

15 Blei, D. M., "Latent dirichlet allocation" 3 : 993-1022, 2003

16 Hoffmann, T., "Latent class models for collaborative filtering" 99 (99): 688-693, 1999

17 Magidson, J., "Latent class models for clustering : A comparison with k-means" 20 (20): 37-44, 2002

18 M.K. Normalini, "Investigating the Impact of Security Factors In E-business and Internet Banking Usage Intention among Malaysians" 대한산업공학회 18 (18): 501-510, 2019

19 Goto, M, "Introduction to Pattern Analysis and Machine Learning" Corona Inc 2014

20 Akaike, H., "Information theory and an extension of the maximum likelihood principle" 267-281, 1973

21 Tsukasa, I., "Improvement of prediction accuracy of the number of customers by latent class model" 1B3-, 2011

22 Si, L., "Flexible mixture model for collaborative filtering" 704-711, 2003

23 Iwata, T., "Fashion coordinates recommender system using photographs from fashion magazines" 2262-2267, 2011

24 Morteza Mohammadi, "Examining the Effect of Marketing Mix Elements on Customer Satisfaction with Mediating Role of Electronic Customer Relationship Management" 대한산업공학회 17 (17): 653-661, 2018

25 Kittisak Jermsittiparsert, "Effect of Service Innovation and Market Intelligence on Supply Chain Performance in Indonesian Fishing Industry" 대한산업공학회 18 (18): 407-416, 2019

26 Train, K., "Discrete Choice Methods with Simulation" Cambridge University Press 2009

27 Tsukasa, I., "Customer behavior prediction system by large scale data fusion in a retail service" 26 (26): 670-681, 2011

28 Green, P. E., "Consumer segmentation via latent class analysis" 3 (3): 170-174, 1976

29 Nakayama, A., "Consideration of sales floor placement in stores using POS data" 48 (48): 100-106, 2003

30 Adalbjörnsson, S., "Conjugate priors for Gaussian emission PLSA recommender systems" 2016

31 Ishigaki, T., "Automatic extraction method of category bell-dependent variable relationships from POS data with department store ID" 56 (56): 77-83, 2011

32 Hagenaars, J. A., "Applied Latent Class Analysis" Cambridge University Press 2002

33 Seiya Nagamori, "An Analytic Model to Represent Relation between Finish Date of Job-Hunting and Time- Series Variation of Entry Tendencies" 대한산업공학회 18 (18): 292-304, 2019

34 Jin, R., "A study of mixture models for collaborative filtering" 9 (9): 357-382, 2006

35 Goto, M., "A predictive model of number of customers for restaurant chain based on Bayesian model averaging" 6 (6): 91-98, 2012

36 Bhatnagar, A., "A latent class segmentation analysis of e-shoppers" 57 (57): 758-767, 2004

37 Suzuki, T., "A design of recommendation based on flexible mixture model considering purchasing interest and postpurchase satisfaction" 64 (64): 570-578, 2014

38 Yusei Yamamoto, "A Proposal for Classification of Document Data with Unobserved Categories Considering Latent Topics" 대한산업공학회 16 (16): 165-174, 2017

39 Masayuki Goto, "A New Latent Class Model for Analysis of Purchasing and Browsing Histories on EC Sites" 대한산업공학회 14 (14): 335-346, 2015