Learning to Rank Query Recommendations by Semantic Similarities

TL;DR

This paper introduces a supervised learning approach to generate query recommendations that either refine or shift the focus of user queries, improving search effectiveness by balancing relevance and diversity based on semantic similarity.

Contribution

It proposes a novel method combining click-based, topic-based, and session-based strategies to identify and rank query recommendations that focus or shift topics, enhancing user search experience.

Findings

The combined method outperforms individual strategies in relevance and diversity.

Significant improvements in recommendation quality demonstrated on Japanese web search logs.

Supervised learning effectively maximizes semantic similarity while diversifying recommendations.

Abstract

Logs of the interactions with a search engine show that users often reformulate their queries. Examining these reformulations shows that recommendations that precise the focus of a query are helpful, like those based on expansions of the original queries. But it also shows that queries that express some topical shift with respect to the original query can help user access more rapidly the information they need. We propose a method to identify from the query logs of past users queries that either focus or shift the initial query topic. This method combines various click-based, topic-based and session based ranking strategies and uses supervised learning in order to maximize the semantic similarities between the query and the recommendations, while at the same diversifying them. We evaluate our method using the query/click logs of a Japanese web search engine and we show that the combination of the three methods proposed is significantly better than any of them taken individually.