Recommending Groups to Users Using User-Group Engagement and Time-Dependent Matrix Factorization
By Xin Wang, Roger Donaldson, Christopher W. Nell, Peter Gorniak, Martin Ester, Jiajun Bu. 2016
51 citations recorded by the source corpus.
Social networks often provide group features to help users with similar interests associate and consume content together. Recommending groups to users poses challenges due to their complex relationship: user-group affinity is typically measured implicitly and varies with time; similarly, group characteristics change as users join and leave. To tackle these challenges, we adapt existing matrix factorization techniques to learn user-group affinity based on two different implicit engagement metrics: (i) which group-provided content users consume; and (ii) which content users provide to groups. To capture the temporally extended nature of group engagement we implement a time-varying factorization. We test the assertion that latent preferences for groups and users are sparse in investigating elastic-net regularization. Experiments using data from DeviantArt indicate that the time-varying implicit engagement-based model provides the best top-K group recommendations, illustrating the benefit of the added model complexity.
Subfields: Information Systems, Statistical and Nonlinear Physics
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Citations in the corpus
- Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017, methodology): “Collaborative filtering methods [22, 25, 28, 31] use either explicit feedback (e.”
- Attributed Network Embedding for Learning in a Dynamic Environment (2017, background): “e proposed online embedding model is motivated by the observation that most of real-world networks, with no exception for aributed networks, oen evolve smoothly in the temporal dimension between two consecutive time steps [2, 13, 25, 48].”