A Study of Context Dependencies in Multi-page Product Search

TL;DR

This paper investigates how short-term and long-term user context influences multi-page product search and introduces a new embedding model that effectively captures these dependencies, improving relevance and satisfaction.

Contribution

It proposes an end-to-end context-aware embedding model that captures both short-term and long-term user contexts in product search, outperforming existing RF models.

Findings

Short-term context significantly improves search performance.

The proposed model outperforms state-of-the-art RF models.

Short-term context is more impactful than long-term context.

Abstract

In product search, users tend to browse results on multiple search result pages (SERPs) (e.g., for queries on clothing and shoes) before deciding which item to purchase. Users' clicks can be considered as implicit feedback which indicates their preferences and used to re-rank subsequent SERPs. Relevance feedback (RF) techniques are usually involved to deal with such scenarios. However, these methods are designed for document retrieval, where relevance is the most important criterion. In contrast, product search engines need to retrieve items that are not only relevant but also satisfactory in terms of customers' preferences. Personalization based on users' purchase history has been shown to be effective in product search. However, this method captures users' long-term interest, which does not always align with their short-term interest, and does not benefit customers with little or no purchase history. In this paper, we study RF techniques based on both long-term and short-term context dependencies in multi-page product search. We also propose an end-to-end context-aware embedding model which can capture both types of context. Our experimental results show that short-term context leads to much better performance compared with long-term and no context. Moreover, our proposed model is more effective than state-of-art word-based RF models.