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- W2000764607 abstract "We examine the cold-start recommendation task in an online retail setting for users who have not yet purchased (or interacted in a meaningful way with) any available items but who have granted access to limited side information, such as basic demographic data (gender, age, location) or social network information (Facebook friends or page likes). We formalize neighborhood-based methods for cold-start collaborative filtering in a generalized matrix algebra framework that does not require purchase data for target users when their side information is available. In real-data experiments with 30,000 users who purchased 80,000+ books and had 9,000,000+ Facebook friends and 6,000,000+ page likes, we show that using Facebook page likes for cold-start recommendation yields up to a 3-fold improvement in mean average precision (mAP) and up to 6-fold improvements in Precision@k and Recall@k compared to most-popular-item, demographic, and Facebook friend cold-start recommenders. These results demonstrate the substantial predictive power of social network content, and its significant utility in a challenging problem - recommendation for cold-start users." @default.
- W2000764607 created "2016-06-24" @default.
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- W2000764607 date "2014-01-01" @default.
- W2000764607 modified "2023-10-06" @default.
- W2000764607 title "Social collaborative filtering for cold-start recommendations" @default.
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- W2000764607 doi "https://doi.org/10.1145/2645710.2645772" @default.
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