Embedding learning of categorical features (e. g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering.
In this paper, we seek to learn highly compact embeddings for large-vocab sparse features in recommender systems (recsys).
Generating the Top-N recommendations from a large corpus is computationally expensive to perform at scale.
We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images.
Recommender Systems have proliferated as general-purpose approaches to model a wide variety of consumer interaction data.
Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the `context' of users' activities on the basis of actions they have performed recently.
Ranked #1 on Recommendation Systems on Steam
Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning `fashion aware' image representations directly, i. e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pre-trained visual features.
Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems.
For another common application scenario with pairwise labels, there have not existed methods for simultaneous feature learning and hash-code learning.