Sparsity Regularization For Cold-Start Recommendation
- URL: http://arxiv.org/abs/2201.10711v3
- Date: Fri, 28 Jan 2022 17:42:41 GMT
- Title: Sparsity Regularization For Cold-Start Recommendation
- Authors: Aksheshkumar Ajaykumar Shah and Hemanth Venkateswara
- Abstract summary: We introduce a novel representation for user-vectors by combining user demographics and user preferences.
We develop a novel sparse adversarial model, SRLGAN, for Cold-Start Recommendation leveraging the sparse user-purchase behavior.
We evaluate the SRLGAN on two popular datasets and demonstrate state-of-the-art results.
- Score: 7.848143873095096
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Recently, Generative Adversarial Networks (GANs) have been applied to the
problem of Cold-Start Recommendation, but the training performance of these
models is hampered by the extreme sparsity in warm user purchase behavior. In
this paper we introduce a novel representation for user-vectors by combining
user demographics and user preferences, making the model a hybrid system which
uses Collaborative Filtering and Content Based Recommendation. Our system
models user purchase behavior using weighted user-product preferences (explicit
feedback) rather than binary user-product interactions (implicit feedback).
Using this we develop a novel sparse adversarial model, SRLGAN, for Cold-Start
Recommendation leveraging the sparse user-purchase behavior which ensures
training stability and avoids over-fitting on warm users. We evaluate the
SRLGAN on two popular datasets and demonstrate state-of-the-art results.
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