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Design of a shopbot and recommender system for bundle purchases [An article from: Decision Support Systems]
Book Details
PublisherElsevier
ISBN / ASINB000PBZWLQ
ISBN-13978B000PBZWL2
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Decision Support Systems, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
The increasing proliferation of online shopping and purchasing has naturally led to a growth in the popularity of comparison-shopping search engines, popularly known as ''shopbots''. We extend the one-product-at-a-time search approach used in current shopbot implementations to consider purchasing plans for a bundle of items. Our approach leverages bundle-based pricing and promotional deals frequently offered by online merchants to extract substantial savings. Interestingly, our approach can also identify ''freebies'' that consumers can obtain at no extra cost. We also develop a model to extend the capability of the current recommendation algorithms that are mainly based on collaborative filtering and item-to-item similarity techniques, to incorporate product price and savings as an additional important factor in making recommendations to shoppers. We develop a practical algorithm that can be employed when the number of items is large or when the real-time nature of shopbot applications dictates quick response rates to consumer queries. A detailed experimental analysis with real-world data from major retailers suggests that the proposed models can provide significant savings for bundle purchasing consumers, and frequently identify freebies for consumers. Together the results underscore the potential benefits that can accrue by incorporating our models into current shopbot systems.
Description:
The increasing proliferation of online shopping and purchasing has naturally led to a growth in the popularity of comparison-shopping search engines, popularly known as ''shopbots''. We extend the one-product-at-a-time search approach used in current shopbot implementations to consider purchasing plans for a bundle of items. Our approach leverages bundle-based pricing and promotional deals frequently offered by online merchants to extract substantial savings. Interestingly, our approach can also identify ''freebies'' that consumers can obtain at no extra cost. We also develop a model to extend the capability of the current recommendation algorithms that are mainly based on collaborative filtering and item-to-item similarity techniques, to incorporate product price and savings as an additional important factor in making recommendations to shoppers. We develop a practical algorithm that can be employed when the number of items is large or when the real-time nature of shopbot applications dictates quick response rates to consumer queries. A detailed experimental analysis with real-world data from major retailers suggests that the proposed models can provide significant savings for bundle purchasing consumers, and frequently identify freebies for consumers. Together the results underscore the potential benefits that can accrue by incorporating our models into current shopbot systems.
