A New Latent Class Model for Analysis of Purchasing and Browsing Histories on EC Sites

Goto, Masayuki; Mikawa, Kenta; Hirasawa, Shigeichi; Kobayashi, Minoru; Suko, Tota; Horii, Shunsuke

doi:10.7232/iems.2015.14.4.335

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…The information is clustered with a latent class analysis approach which results in seven segments. Goto et al (2015) propose a method based on latent class analysis that clusters items and customers. They assume valuable users purchase more often only browsing and valuable products are bought more often.…”

Section: Tablementioning

confidence: 99%

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A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

Gomes

Meisen

2023

Inf Syst E-Bus Manage

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The importance of customer-oriented marketing has increased for companies in recent decades. With the advent of one-customer strategies, especially in e-commerce, traditional mass marketing in this area is becoming increasingly obsolete as customer-specific targeting becomes realizable. Such a strategy makes it essential to develop an underlying understanding of the interests and motivations of the individual customer. One method frequently used for this purpose is segmentation, which has evolved steadily in recent years. The aim of this paper is to provide a structured overview of the different segmentation methods and their current state of the art. For this purpose, we conducted an extensive literature search in which 105 publications between the years 2000 and 2022 were identified that deal with the analysis of customer behavior using segmentation methods. Based on this paper corpus, we provide a comprehensive review of the used methods. In addition, we examine the applied methods for temporal trends and for their applicability to different data set dimensionalities. Based on this paper corpus, we identified a four-phase process consisting of information (data) collection, customer representation, customer analysis via segmentation and customer targeting. With respect to customer representation and customer analysis by segmentation, we provide a comprehensive overview of the methods used in these process steps. We also take a look at temporal trends and the applicability to different dataset dimensionalities. In summary, customer representation is mainly solved by manual feature selection or RFM analysis. The most commonly used segmentation method is k-means, regardless of the use case and the amount of data. It is interesting to note that it has been widely used in recent years.

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Section: Tablementioning

confidence: 99%

“…They test their hybrid approach on two datasets in which one dataset contains information about 101,532 customers. Another one comes fromGoto et al (2015) where they apply a latent class model on 37,278,907 browsing actions from 99,924 users Abdolvand et al (2015). apply k-means on 25,000 bank customers.…”

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confidence: 99%

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

Gomes

Meisen

2023

Inf Syst E-Bus Manage

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“…Several works related with the probability model with latent topics have been developed, for example, Si and Jin (2003) proposed the flexible mixture model (FMM) and Blei and Jordan (2003) proposed the Latent Dirichlet Allocation (LDA). Goto et al (2015) proposed a latent class model representing customers' browsing and purchasing behaviors toward items on an EC site. In this model, the behavior of customers' purchasing and viewing of items is represented by a two-dimensional vector , , which is composed of the number of purchases and the number of views .…”

Section: Latent Class Modelmentioning

confidence: 99%

<b>An Analytical Model of Relation between Browsing and Entry Activities on an Internet Portal Site for Job-hunting</b>

Sugiyama

Arai

Yang

et al. 2019

TQS

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In recent years, many companies conducting recruitment activities and many students looking for a job use internet portal sites for job-hunting in Japan. Companies can post their basic information on individual company pages and recruit applications from students. On the other hand, student users can gauge corporate attractiveness by browsing individual company pages on a job-hunting site and can make entries to companies of interest. Therefore, a large amount of their behavior history data is accumulated on the site. There are several studies on prediction of users' entries to companies and analysis of preference using user attribute information and entry history data. However, in the conventional researches, browsing activities on individual company pages existing in the background of the user's entry were not considered, so the relation between browsing and making entries has not been studied. This research proposes a latent class model for analyzing the relation between browsing company pages and making entries to companies. The proposed model enables clarification of target users and consideration of effective promotion activities. Through a demonstrative analysis using actual data on a major jobhunting website in Japan, we show the effectiveness of the proposed model.

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“…A general approach for recommending fashion items is to explain each user's taste with item attributes such as color, material, and price of items, or/and user profile information such as age and gender [2]. One of the most recent studies [3] generated user clusters based on color, age range, and price range, and scored a user's preference for an item as a function of the preference of a cluster containing the user by reinforcement learning to adapt to the user's behavior.…”

Section: Introductionmentioning

confidence: 99%

Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

Lee

Kim

2019

Applied Sciences

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Although fashion-related products account for most of the online shopping categories, it becomes more difficult for users to search and find products matching their taste and needs as the number of items available online increases explosively. Personalized recommendation of items is the best method for both reducing user effort on searching for items and expanding sales opportunity for sellers. Unfortunately, experimental studies and research on fashion item recommendation for online shopping users are lacking. In this paper, we propose a novel recommendation framework suitable for online apparel items. To overcome the rating sparsity problem of online apparel datasets, we derive implicit ratings from user log data and generate predicted ratings for item clusters by user-based collaborative filtering. The ratings are combined with a network constructed by an item click trend, which serves as a personalized recommendation through a random walk. An empirical evaluation on a large-scale real-world dataset obtained from an apparel retailer demonstrates the effectiveness of our method.

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A New Latent Class Model for Analysis of Purchasing and Browsing Histories on EC Sites

Cited by 8 publications

References 9 publications

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

<b>An Analytical Model of Relation between Browsing and Entry Activities on an Internet Portal Site for Job-hunting</b>

Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

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