TY - JOUR
T1 - Recommending tasks based on search queries and missions
AU - Garigliotti, Dario
AU - Balog, Krisztian
AU - Hose, Katja
AU - Bjerva, Johannes
PY - 2023/5/17
Y1 - 2023/5/17
N2 - Web search is an experience that naturally lends itself to recommendations, including query suggestions and related entities. In this article, we propose to recommend specific tasks to users, based on their search queries, such as planning a holiday trip or organizing a party. Specifically, we introduce the problem of query-based task recommendation and develop methods that combine well-established term-based ranking techniques with continuous semantic representations, including sentence representations from several transformer-based models. Using a purpose-built test collection, we find that our method is able to significantly outperform a strong text-based baseline. Further, we extend our approach to using a set of queries that all share the same underlying task, referred to as search mission, as input. The study is rounded off with a detailed feature and query analysis.
AB - Web search is an experience that naturally lends itself to recommendations, including query suggestions and related entities. In this article, we propose to recommend specific tasks to users, based on their search queries, such as planning a holiday trip or organizing a party. Specifically, we introduce the problem of query-based task recommendation and develop methods that combine well-established term-based ranking techniques with continuous semantic representations, including sentence representations from several transformer-based models. Using a purpose-built test collection, we find that our method is able to significantly outperform a strong text-based baseline. Further, we extend our approach to using a set of queries that all share the same underlying task, referred to as search mission, as input. The study is rounded off with a detailed feature and query analysis.
KW - Information Retrieval
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85160558828&partnerID=8YFLogxK
U2 - 10.1017/S1351324923000219
DO - 10.1017/S1351324923000219
M3 - Journal article
SN - 1469-8110
VL - 36
SP - 1
EP - 25
JO - Natural Language Engineering
JF - Natural Language Engineering
IS - 2
ER -