Summary of Learning to Ask: Conversational Product Search Via Representation Learning, by Jie Zou et al.
Learning to Ask: Conversational Product Search via Representation Learning
by Jie Zou, Jimmy Xiangji Huang, Zhaochun Ren, Evangelos Kanoulas
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper presents ConvPS, a novel conversational product search model designed to assist users in finding desired products through online shopping platforms like Amazon and AliExpress. Traditional product search methods rely on keywords, whereas conversational product search enables user-machine conversations, collecting explicit feedback on preferences. Existing approaches either model conversations independently or suffer from vocabulary mismatches. ConvPS jointly learns semantic representations of users, queries, items, and conversations using a unified generative framework, then retrieves target items in the latent semantic space. The proposed model also incorporates greedy and explore-exploit strategies to ask high-performance questions during conversations. Experimental results show that ConvPS outperforms state-of-the-art baselines, offering a promising approach for constructing accurate conversational product search systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine shopping online like having a conversation with a friend! You can tell the website what you’re looking for and get recommendations based on your preferences. This paper proposes a new way to do that called ConvPS. It’s a model that helps users find products they want by understanding their conversations. The current methods either don’t consider the user or product well, which is not good enough. ConvPS does better because it learns about both the user and product at the same time. This approach can ask smart questions to help you get what you’re looking for. The results show that ConvPS performs much better than other models. |
