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The cold-start problem is a common challenge for most recommender systems.The practical application of most cold-start methods is hindered by thedeficiency in auxiliary content information for users. Moreover, most methodsnecessitate simultaneous updates to the extensive parameters of recommendermodels, leading to significant training costs, particularly in large-scaleindustrial scenarios. We observe that the model can generate expressiveembeddings for warm users with relatively more interactions. Initially, theseusers were cold-start users, and after transitioning to warm users, theyexhibit clustering patterns in their embeddings with consistent initialinteractions. Based on this motivation, we propose a Variational Mappingapproach for cold-start user Recommendation (VM-Rec), mapping from few initialinteractions to expressive embeddings for cold-start users. Specifically, weencode the initial interactions into a latent representation, where eachdimension disentangledly signifies the degree of association with each warmuser. Subsequently, we utilize this latent representation as the parameters forthe mapping function, mapping (decoding) it into an expressive embedding, whichcan be integrated into a pre-trained recommender model directly. Our method isevaluated on three datasets using the same base model, demonstrating superiorperformance compared to other popular cold-start methods.

VM-Rec: A Variational Mapping Approach for Cold-start User Recommendation