VM-Rec: A Variational Mapping Approach for Cold-start User Recommendation.
CoRR(2023)
摘要
The cold-start problem is a common challenge for most recommender systems.
With extremely limited interactions of cold-start users, conventional
recommender models often struggle to generate embeddings with sufficient
expressivity. Moreover, the absence of auxiliary content information of users
exacerbates the presence of challenges, rendering most cold-start methods
difficult to apply. To address this issue, our motivation is based on the
observation that if a model can generate expressive embeddings for existing
users with relatively more interactions, who were also initially cold-start
users, then we can establish a mapping from few initial interactions to
expressive embeddings, simulating the process of generating embeddings for
cold-start users. Based on this motivation, we propose a Variational Mapping
approach for cold-start user Recommendation (VM-Rec). Firstly, we generate a
personalized mapping function for cold-start users based on their initial
interactions, and parameters of the function are generated from a variational
distribution. For the sake of interpretability and computational efficiency, we
model the personalized mapping function as a sparse linear model, where each
parameter indicates the association to a specific existing user. Consequently,
we use this mapping function to map the embeddings of existing users to an
embedding of the cold-start user in the same space. The resulting embedding has
similar expressivity to that of existing users and can be directly integrated
into a pre-trained recommender model to predict click through rates or ranking
scores. We evaluate our method based on three widely used recommender models as
pre-trained base recommender models, outperforming four popular cold-start
methods on two datasets under the same base model.
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关键词
variational mapping approach,recommendation,vm-rec,cold-start
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