DeepNaniNet claims up to 7x Recall@100 over weighted matrix factorization on a new anime dataset by combining BERT text embeddings, a graph network, and a content-basket autoencoder, but the provided manuscript does not show the experiments.
Solving the Cold-Start Problem in Recommender Systems with Social Tags
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abstract
In this paper, based on the user-tag-object tripartite graphs, we propose a recommendation algorithm, which considers social tags as an important role for information retrieval. Besides its low cost of computational time, the experiment results of two real-world data sets, \emph{Del.icio.us} and \emph{MovieLens}, show it can enhance the algorithmic accuracy and diversity. Especially, it can obtain more personalized recommendation results when users have diverse topics of tags. In addition, the numerical results on the dependence of algorithmic accuracy indicates that the proposed algorithm is particularly effective for small degree objects, which reminds us of the well-known \emph{cold-start} problem in recommender systems. Further empirical study shows that the proposed algorithm can significantly solve this problem in social tagging systems with heterogeneous object degree distributions.
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Privacy Preserving Inference of Personalized Content for Out of Matrix Users
DeepNaniNet claims up to 7x Recall@100 over weighted matrix factorization on a new anime dataset by combining BERT text embeddings, a graph network, and a content-basket autoencoder, but the provided manuscript does not show the experiments.