# Query-aware Tip Generation for Vertical Search

arXiv: https://arxiv.org/abs/2010.09254 · HTML: https://mlsanity.com/paper/2010.09254/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Personalized Prompt Learning for Explainable Recommendation](https://mlsanity.com/paper/2202.07371/) (2022)
- [Generating Tips from Song Reviews: A New Dataset and Framework](https://mlsanity.com/paper/2205.06985/) (2022)
- [Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models](https://mlsanity.com/paper/2312.15490/) (2023)

## Abstract (arXiv)

As a concise form of user reviews, tips have unique advantages to explain the search results, assist users' decision making, and further improve user experience in vertical search scenarios. Existing work on tip generation does not take query into consideration, which limits the impact of tips in search scenarios. To address this issue, this paper proposes a query-aware tip generation framework, integrating query information into encoding and subsequent decoding processes. Two specific adaptations of Transformer and Recurrent Neural Network (RNN) are proposed. For Transformer, the query impact is incorporated into the self-attention computation of both the encoder and the decoder. As for RNN, the query-aware encoder adopts a selective network to distill query-relevant information from the review, while the query-aware decoder integrates the query information into the attention computation during decoding. The framework consistently outperforms the competing methods on both public and real-world industrial datasets. Last but not least, online deployment experiments on Dianping demonstrate the advantage of the proposed framework for tip generation as well as its online business values.