A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data
This addresses the challenge of optimizing product titles for mobile E-commerce users and merchants, though it is incremental as it builds on existing sequence-to-sequence and attention methods.
The paper tackles the problem of compressing lengthy product titles in E-commerce by proposing a multi-task learning approach that uses user search log data, resulting in improved compression quality and online business values as demonstrated in experiments.
It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.