Recommending groups to users using user-group engagement and time-dependent matrix factorization
作者： Martin Ester , Peter Gorniak , Jiajun Bu , Roger Donaldson , Xin Wang
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摘要: Social networks often provide group features to help users with similar interests associate and consume content together. Recommending groups poses challenges due their complex relationship: user-group affinity is typically measured implicitly varies time; similarly, characteristics change as join leave. To tackle these challenges, we adapt existing matrix factorization techniques learn based on two different implicit engagement metrics: (i) which group-provided consume; (ii) groups. capture the temporally extended nature of implement a time-varying factorization. We test assertion that latent preferences for are sparse in investigating elastic-net regularization. Our experiments indicate engagement-based model provides best top-K recommendations, illustrating benefit added complexity.
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