Three Unique Architectures For Deep Learning Based Recommendation Systems

Deep learning based recommendation system architectures make use of multiple simpler approaches in order to remediate the shortcomings of any single approach to extracting, transforming and vectorizing a large corpus of data into a useful recommendation for an end user. High-level extraction architectures are useful for categorization, but lack accuracy. Low-level extraction approaches will produce committed decisions about what to recommend, but, since they lack context, their recommendations may be banal, repetitive or even recursive, creating unintelligent'content bubbles' for the user. High level architectures cannot'zoom in' meaningfully, and low-level architectures cannot'step back' to understand the bigger picture that the data is presenting. In this article we'll take a look at three unique approaches that reconcile these two needs into effective and unified frameworks suitable for recommender systems.