Dual-view Contrastive Learning for Auction Recommendation
Authors: 
Dan Ni Ren, Leong Hou U, and Wei LiuAuthors Info & Claims
Dan Ni Ren, Leong Hou U, and Wei LiuAuthors Info & ClaimsOctober 2023
Pages 2146 - 2155
Abstract
Recommendation systems in auction platforms like eBay function differently in comparison to those found in traditional trading platforms. The bidding process involves multiple users competing for a product, with the highest bidder winning the item. As a result, each transaction is independent and characterized by varying transaction prices. The individual nature of auction items means that users cannot purchase identical items, adding to the uniqueness of the purchasing history. Bidders in auction systems rely on their judgment to determine the value of a product, as bidding prices reflect preferences rather than cost-free actions like clicking or collecting. Conventional methodologies that heavily rely on user-item purchase history are ill-suited to handle these unique and extreme product features. Unfortunately, prior recommendation approaches have failed to give due attention to the contextual intricacies of auction items, thereby missing out on the full potential of the invaluable bidding record at hand.
This paper introduces a novel contrastive learning approach for auction recommendation, addressing the challenges of data sparsity and uniqueness in auction recommendation. Our method focuses on capturing multiple behavior relations and item context through contrastive pairs construction, contrastive embedding, and contrastive optimization techniques from both user and item perspectives. By overcoming the limitations of previous approaches, our method delivers promising results on two auction datasets, highlighting the practicality and effectiveness of our model.
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Index Terms
- Dual-view Contrastive Learning for Auction Recommendation
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Information & Contributors
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Published In
October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
- General Chairs:
- Ingo Frommholz,
- Frank Hopfgartner,
- Mark Lee,
- Michael Oakes,
- Program Chairs:
- Mounia Lalmas,
- Min Zhang,
- Rodrygo Santos
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Publication History
Published: 21 October 2023
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- he Science and Technology Development Fund Macau SAR
- National Natural Science Foundation of China
- he Research Grant of University of Maca
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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management
October 21 - 25, 2023
Birmingham, United Kingdom
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