Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Autonomous Agents and Multi-Agent Systems
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Frictionless Commerce? A Comparison of Internet and Conventional Retailers
Management Science
Management Science
IEEE Transactions on Knowledge and Data Engineering
A semantic-expansion approach to personalized knowledge recommendation
Decision Support Systems
Multidimensional credibility model for neighbor selection in collaborative recommendation
Expert Systems with Applications: An International Journal
Handling sequential pattern decay: Developing a two-stage collaborative recommender system
Electronic Commerce Research and Applications
Recommender system based on workflow
Decision Support Systems
Source credibility model for neighbor selection in collaborative web content recommendation
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Designing a cross-language comparison-shopping agent
Decision Support Systems
Information Systems Research
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Comparison shopping agents and online price dispersion: a search cost based explanation
Journal of Theoretical and Applied Electronic Commerce Research
Discovering patterns of online purchasing behaviour and a new-product-launch strategy
Expert Systems: The Journal of Knowledge Engineering
A trust-semantic fusion-based recommendation approach for e-business applications
Decision Support Systems
Introducing spatial context in comparative pricing and product search
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
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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.