12,939 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks
Recommendation models are vital in delivering personalized user experiences
by leveraging the correlation between multiple input features. However, deep
learning-based recommendation models often face challenges due to evolving user
behaviour and item features, leading to covariate shifts. Effective
cross-feature learning is crucial to handle data distribution drift and
adapting to changing user behaviour. Traditional feature interaction techniques
have limitations in achieving optimal performance in this context.
This work introduces Ad-Rec, an advanced network that leverages feature
interaction techniques to address covariate shifts. This helps eliminate
irrelevant interactions in recommendation tasks. Ad-Rec leverages masked
transformers to enable the learning of higher-order cross-features while
mitigating the impact of data distribution drift. Our approach improves model
quality, accelerates convergence, and reduces training time, as measured by the
Area Under Curve (AUC) metric. We demonstrate the scalability of Ad-Rec and its
ability to achieve superior model quality through comprehensive ablation
studies
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