Hybrid Product Recommender System for Apparel Retailing Customers

Liangxing, Yu; Dong, Aihua

doi:10.1109/icie.2010.91

Cited by 13 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Specifically, they focus on how to adapt recommender systems to take contextual information (e.g., the current season or trends) into account. Similarly, Liangxing and Aihua [16] outline the opportunity for fashion retailers to identify their customers using RFID-based membership cards that allow them to provide personalized fashion recommendations to their customers. Kang et al [14] propose a recommender approach, which takes visual aspects of fashion products into account.…”

Section: Related Workmentioning

confidence: 99%

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

Wölbitsch¹,

Hasler²,

Walk³

et al. 2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

View full text Add to dashboard Cite

Over the course of the last decade, online retailers have demonstrated that knowledge about customer preferences and shopping patterns is an important asset for running a successful business. For example, customer preferences and shopping histories are the foundation for recommender systems that support the search for relevant products to buy online. With the increasing adoption of modern technologies, traditional retailers are able to collect similar data about customer behavior in their stores. For example, smart fitting rooms allow to track interactions of customers with products beyond the scope of a traditional retail store. In this paper we explore how customers of a large international fashion retailer buy products online and in brick-and-mortar stores, and uncover significant differences between the two domains. In particular, we find that online customers frequently focus on buying products from one specific category, whereas customers in brick-and-mortar stores often buy a more diverse range of product types. Further, we investigate products that customers take into fitting rooms, and we find that they frequently deviate from, and complement purchases. Finally, we demonstrate how our findings impact practical applications, illustrated using recommender systems, and discuss how shopping baskets from different domains can be leveraged.

show abstract

Section: Related Workmentioning

confidence: 99%

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

Wölbitsch¹,

Hasler²,

Walk³

et al. 2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

View full text Add to dashboard Cite

show abstract

“…If an item is purchased then its priority level will be 1. If an item is selected but not purchased then it‫׳‬s value will be computed via probability of its purchase [13].…”

Section: Collaborative Filtering (Cf)mentioning

confidence: 99%

An Effective Model for Improving the Quality of Recommender Systems in Mobile E-Tourism

Mohammadnezhad¹

2012

IJCSIT

View full text Add to dashboard Cite

show abstract

“…A third type of RS that combines these two approaches and is usually denoted as hybrid RS. Hybrid approaches have been applied to many contexts, such as those presented in [3], [4]. This work is based on a content-based RS.…”

Section: Introductionmentioning

confidence: 99%