Summary of Grainrec: Graph and Attention Integrated Approach For Real-time Session-based Item Recommendations, by Bhavtosh Rath et al.
GRAINRec: Graph and Attention Integrated Approach for Real-Time Session-Based Item Recommendations
by Bhavtosh Rath, Pushkar Chennu, David Relyea, Prathyusha Kanmanth Reddy, Amit Pande
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in session-based recommendation models have led to significant performance improvements, but these sophisticated models can be challenging to implement in real-time. To address this challenge, we propose GRAINRec: a Graph and Attention Integrated model that generates recommendations in real-time for item recommendations in online retail. Our approach considers the importance of all items in the session together, allowing us to predict relevant recommendations dynamically as the session evolves. We also introduce a heuristic approach for real-time inferencing that meets Target’s service level agreement (SLA). Evaluation results show an average improvement of 1.5% across offline metrics, and A/B tests demonstrate a 10% increase in click-through rate and 9% increase in attributable demand over two weeks. Extensive ablation studies are conducted to analyze our model’s performance for different parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine getting personalized recommendations while shopping online, but these recommendations need to be generated quickly so you don’t have to wait. Researchers have made big improvements in this area, but it’s still a challenge to make these recommendations in real-time. To solve this problem, they created a new model called GRAINRec that can generate recommendations fast and accurately. This model is designed for online retail, where customers browse and buy items online. The model looks at the whole shopping session, including all the items you’ve viewed or added to your cart, to make smart recommendations. It even has a special trick to make sure these recommendations are generated quickly enough. The results show that this new approach works well, with a 10% increase in people clicking on recommended products and a 9% increase in sales. |
Keywords
* Artificial intelligence * Attention
