DOI

As search systems gradually turn into intelligent personal assistants, users increasingly resort to a search engine to accomplish a complex task, such as planning a trip, renting an apartment, or investing in stocks. A key challenge for the search engine is to understand the user's underlying task given a sample query like “tickets to Panama”, “studios in los angeles”, or “spotify stocks”, and to suggest other queries to help the user complete the task. In this paper, we investigate several strategies for query recommendation by traversing a semantically annotated query log using a mixture of explicit and latent representations of entire queries and of query segments. Our results demonstrate the efectiveness of these strategies in terms of utility and diversity, as well as their complementarity, with signifcant improvements compared to state-of-the-art query recommendation baselines adapted for this task.

Original languageEnglish
Title of host publicationRecSys 2019 - 13th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery (ACM)
Pages511-515
Number of pages5
ISBN (Electronic)9781450362436
DOIs
Publication statusPublished - 10 Sep 2019
Event13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark
Duration: 16 Sep 201920 Sep 2019

Conference

Conference13th ACM Conference on Recommender Systems, RecSys 2019
CountryDenmark
CityCopenhagen
Period16/09/1920/09/19

    Research areas

  • Query embeddings, Query recommendations, Task understanding

ID: 62482931