Academic journal article Journal of Electronic Commerce Research

A Conditional Feature Utilization Approach to Itemset Retrieval in Online Shopping Services

Academic journal article Journal of Electronic Commerce Research

A Conditional Feature Utilization Approach to Itemset Retrieval in Online Shopping Services

Article excerpt

(ProQuest: ... denotes formulae omitted.)

1. Introduction

Owing to the recent proliferation of various types of online shopping services such as open markets, Internet auctions, and social commerce where sellers are allowed to vend their items with their own pricing strategies, there exist many items with diverse prices and various descriptions for a single product type across many online shopping malls [Ramachandran et al. 2011; Wu et al. 2011]. In Google Shopping, for instance, a product type named "Sony Micro Vault USB 16G" is sold in the forms of 65 distinct items with different prices and descriptions. While the availability of multiple items for a single product type offers a wide variety of purchase choices to a customer, it makes difficult for a customer to identify the items belonging to the same product type of interest.

To enhance the customer's shopping experience, two types of search services, namely item search and itemset search, are often provided. The item search aims to retrieve relevant items to a user query that usually consists of words or phrases while the itemset search seeks to find the set of items belonging to the same product type as that of an item found to be interesting to a customer for the purpose of price comparison. This paper focuses on the itemset retrieval problem, and we refer a given item as a query item and the set of items to be suggested as a target itemset against the query item. In Figure 1, the relationship between a product type and items as well as the relationship between a query item and its target itemset are illustrated.

Through facilitating automatic retrieval of the target itemset for a query item, customers are provided with comparison results for the items from the same product type, resulting in reduced item search costs [Tan et al. 2010]. In the meantime, the automatic itemset retrieval method is also beneficial to service providers as they are no longer required to prepare all the possible target itemsets in advance. Moreover, the target itemsets can be further utilized to improve the performances of item ranking [Kim et al. 2012b], item recoimnendation [Linden et al. 2003], and item bundling [Garfinkel et al. 2008] by allowing exploration of the relationships among items.

There have been many research results pertaining to the itemset retrieval. Yet, they have limited applicability due to the requirement of information such as the known number of itemsets [Kannan et al. 2011b; Kim et al. 2012a], the predefined hierarchical structure of itemsets [Abbott et al. 2011; Benjelloun et al. 2009], and the prior knowledge for adjusting model parameters [Kopcke et al. 2010; Wong et al. 2008].

Supervised approaches presented in [Abbott et al. 2011; Geng et al. 2012; Kim et al. 2013b] that require training data on item membership against an itemset may not be viable options for small and medium sized shopping services due to the significant amount of time and cost involved for obtaining the training data [Kannan et al. 2011a]. Furthermore, due to the dynamic nature of an online shopping service where a large number of items are newly created and frequently updated, maintaining such data up-to-date is a challenging task [Tang et al. 2001].

Accordingly, we consider a problem of target itemset retrieval that returns the items in the target itemset against a query item by only using textual descriptions and prices of items without relying on the aforementioned additional information. In this paper, each item is represented by its price and textual description, a short text snippet describing a specific product type. Note that the target itemset retrieval we consider is a special case of the itemset construction problem which groups items of same product type into an itemset [Lynch et al. 2000]. Utilizing only the textual description and price as features of an item facilitates wide usage of the proposed model since they are mandatory information in most online shopping services and other data such as product specification and seller detail are often unavailable. …

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