Summary of Non-autoregressive Personalized Bundle Generation, by Wenchuan Yang et al.
Non-autoregressive Personalized Bundle Generation
by Wenchuan Yang, Cheng Yang, Jichao Li, Yuejin Tan, Xin Lu, Chuan Shi
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 The personalized bundle generation problem aims to create a preferred bundle for users from numerous candidate items. This paper proposes a novel non-autoregressive mechanism called BundleNAT that outputs the targeted bundle in one-shot without relying on inherent order. The model uses pre-training techniques and graph neural networks to embed user-based preferences and item-based compatibility information, then extracts global dependency patterns using a self-attention encoder. The decoding architecture is permutation-equivariant, allowing for direct output of the desired bundle. Experimental results on three real-world datasets show significant improvements over current state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create a personalized bundle for users by choosing items they like best. It’s like making a playlist or picking the perfect gift! The researchers created a new way to do this without relying on how things are ordered, which makes it faster and more accurate. They used special techniques and computer networks to understand what people like and what items go well together. Then, they designed a special architecture that can quickly find the best bundle for each user. Tests showed their method did much better than other methods, with big improvements in finding the right items. |
Keywords
» Artificial intelligence » Autoregressive » Encoder » One shot » Self attention
