Query-aware Tip Generation for Vertical Search

TL;DR

This paper introduces a query-aware tip generation framework for vertical search that incorporates query information into neural network models, significantly improving tip relevance and effectiveness in search scenarios.

Contribution

It proposes novel adaptations of Transformer and RNN models that integrate query context into tip generation, enhancing relevance over existing methods.

Findings

Outperforms existing tip generation methods on multiple datasets.

Demonstrates effectiveness in real-world industrial deployment.

Shows online business value through deployment experiments.

Abstract

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.