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ABCPRec: Adaptively Bridging Consumer and Producer Roles for User-Generated Content Recommendation (SIGIR 2019)

Senior Researcher - National Institute of Advanced Industrial Science and Technology (AIST)
Aug. 4, 2019
ABCPRec: Adaptively Bridging Consumer and Producer Roles for User-Generated Content Recommendation (SIGIR 2019)
ABCPRec: Adaptively Bridging Consumer and Producer Roles for User-Generated Content Recommendation (SIGIR 2019)
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ABCPRec: Adaptively Bridging Consumer and Producer Roles for User-Generated Content Recommendation (SIGIR 2019)

  1. ABCPRec: Adaptively Bridging Consumer and Producer Roles for User-Generated Content Recommendation What is the characteristic of UGC? How to recommend UGC to a user? How to learn consumer/producer vectors? What are the experimental results? C Non UGC: User = Consumer UGC: User = Consumer + Producer C P 𝝂𝝂𝑢𝑢 𝑝𝑝 A user has two vectors corresponding to two roles: consumer and producer. P C 𝝂𝝂𝑢𝑢 𝑐𝑐 𝑢𝑢 ? 𝑖𝑖 C 𝝂𝝂𝑢𝑢 𝑐𝑐 = �𝑥𝑥𝑢𝑢𝑢𝑢 = 𝛼𝛼 + 𝛽𝛽𝑢𝑢 + 𝛽𝛽𝑖𝑖 + 𝝂𝝂𝑢𝑢 𝑐𝑐 , 𝜸𝜸𝑖𝑖 + 𝝂𝝂𝑢𝑢 𝑐𝑐 , 𝝂𝝂𝑝𝑝𝑖𝑖 𝑝𝑝 ? where 𝝂𝝂𝑢𝑢 𝑐𝑐 , 𝜸𝜸𝑖𝑖 𝑝𝑝 is the affinity between user 𝑢𝑢 and item 𝑖𝑖 where 𝝂𝝂𝑢𝑢 𝑐𝑐 , 𝝂𝝂𝑝𝑝𝑖𝑖 𝑝𝑝 is the affinity between user 𝑢𝑢 and 𝑖𝑖’s producer 𝑝𝑝𝑖𝑖 𝜸𝜸𝒊𝒊 𝝂𝝂𝑢𝑢 𝑝𝑝 𝑝𝑝𝑖𝑖 P 𝒖𝒖’s consumed items 𝝂𝝂𝑢𝑢 𝑐𝑐 𝝂𝝂𝑢𝑢 𝑝𝑝 𝒖𝒖’s produced items Consume Consume Target user 𝑢𝑢 Consume Consume Target user 𝑢𝑢 𝝂𝝂𝑢𝑢 𝑐𝑐 𝝂𝝂𝑢𝑢 𝑝𝑝  If 𝒖𝒖’s nature as a consumer is similar to that as a producer, 𝝂𝝂𝑢𝑢 𝑐𝑐 and 𝝂𝝂𝑢𝑢 𝑝𝑝 should be close.  Compute the similarity based on the overlap between the users who consumed 𝒖𝒖’s consumed items and those who consumed 𝒖𝒖’s produced items. If the overlap is big, 𝝂𝝂𝑢𝑢 𝑐𝑐 and 𝝂𝝂𝑢𝑢 𝑝𝑝 should be close. If the overlap is small, 𝝂𝝂𝑢𝑢 𝑐𝑐 and 𝝂𝝂𝑢𝑢 𝑝𝑝 should not be close. 𝑲𝑲 PopRec BPR Vista FMs CPRec NBCPRec ABCPRecH1 ABCPRecH2 Flickr 20 0.6737 0.8698 0.8436 0.8764 0.8563 0.8839 0.8861 0.8900 50 0.6737 0.8772 0.8435 0.8822 0.8664 0.8937 0.8949 0.8992 80 0.6737 0.8777 0.8394 0.8810 0.8712 0.8955 0.8988 0.9028 Reddit 20 0.6392 0.8713 0.8829 0.8960 0.9138 0.9209 0.9296 0.9340 50 0.6392 0.8721 0.8918 0.8999 0.9201 0.9302 0.9346 0.9391 80 0.6392 0.8709 0.8946 0.9001 0.9211 0.9322 0.9376 0.9408  CPRec is the state-of-the-art method for UGC recommendations proposed at RecSys’18.  ABCPRecH1/ABCPRecH2 focus on the users who consumed 𝒖𝒖’s consumed/produced items.  ABCPRec statistically outperformed CPRec in terms of AUC. User-item matrix 𝒖𝒖’s consumed items 𝒖𝒖’s produced items UGC is content produced by ordinary people rather than by professionals and distributed on the Web. National Institute of Advanced Industrial Science and Technology (AIST)Kosetsu Tsukuda, Satoru Fukayama, Masataka Goto
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