Effective recommendation systems rely on capturing user preferences, often
requiring incorporating numerous features such as universally unique
identifiers (UUIDs) of entities. However, the exceptionally high cardinality of
UUIDs poses a significant challenge in terms of model degradation and increased
model size due to sparsity. This paper presents two innovative techniques to
address the challenge of high cardinality in recommendation systems.
Specifically, we propose a bag-of-words approach, combined with layer sharing,
to substantially decrease the model size while improving performance. Our
techniques were evaluated through offline and online experiments on Uber use
cases, resulting in promising results demonstrating our approach’s
effectiveness in optimizing recommendation systems and enhancing their overall
performance.
Improving Recommendation Systems with Techniques to Address High Cardinality
Recommendation systems are integral to many applications and platforms, from e-commerce websites to streaming services. These systems rely on capturing user preferences to provide personalized recommendations, which often requires incorporating numerous features such as universally unique identifiers (UUIDs) of entities. However, the exceptionally high cardinality of UUIDs poses significant challenges for these systems.
One of the main challenges presented by high cardinality is model degradation. When a recommendation model has to handle a large number of UUIDs, the model performance can start to degrade. This is due to the sparsity of the data, with many UUIDs having limited or no interactions associated with them. As a result, the model struggles to accurately learn and make predictions based on these sparse features.
In addition to model degradation, high cardinality also increases the model size. Each UUID needs to be represented as a separate feature in the model, which grows exponentially as the number of distinct UUIDs increases. This can lead to memory and computational inefficiencies, making it challenging to scale recommendation systems.
To overcome these challenges, this paper presents two innovative techniques: a bag-of-words approach and layer sharing. The bag-of-words approach involves treating UUIDs as words and representing them as vectors. This allows the recommendation system to leverage techniques used in natural language processing, such as word embeddings, to capture semantic relationships between UUIDs. By doing so, the model can better generalize and make predictions based on similar UUIDs, even if they have limited interactions.
The second technique, layer sharing, reduces the model size by sharing layers across different UUIDs. Instead of creating separate layers for each UUID, the recommendation system can share lower layers that capture general features common to multiple UUIDs. This not only decreases the overall model size but also improves computational efficiency by reducing redundant computations.
The effectiveness of these techniques was evaluated through offline and online experiments on Uber use cases. The results showed promising improvements in both model size reduction and performance enhancement. By reducing model size, the techniques make it easier to handle high cardinality, allowing recommendation systems to scale more efficiently. Furthermore, by improving performance despite the sparsity of data, these techniques enable more accurate and relevant recommendations for users.
What is particularly noteworthy about these techniques is their multi-disciplinary nature. The bag-of-words approach borrows from natural language processing techniques, applying them to the problem of high cardinality in recommendation systems. This cross-pollination of ideas and methodologies between different domains can often lead to innovative solutions with significant impact.
In conclusion, the presented techniques offer valuable insights into addressing the challenge of high cardinality in recommendation systems. By utilizing a bag-of-words approach and layer sharing, these techniques optimize model size, improve performance, and enable more efficient scaling. Furthermore, they highlight the importance of multidisciplinary approaches in solving complex problems and driving innovation in various domains.