Generating recommendations for entity-oriented exploratory search
- URL: http://arxiv.org/abs/2204.00743v1
- Date: Sat, 2 Apr 2022 02:19:47 GMT
- Title: Generating recommendations for entity-oriented exploratory search
- Authors: David Wadden, Nikita Gupta, Kenton Lee, Kristina Toutanova
- Abstract summary: We introduce the task of recommendation set generation for entity-oriented exploratory search.
We build a text-to-text model capable of generating a collection of recommendations directly.
We train the model to generate recommendation sets which optimize a cost function designed to encourage comprehensiveness, interestingness, and non-redundancy.
- Score: 23.62142250257486
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce the task of recommendation set generation for entity-oriented
exploratory search. Given an input search query which is open-ended or
under-specified, the task is to present the user with an easily-understandable
collection of query recommendations, with the goal of facilitating domain
exploration or clarifying user intent. Traditional query recommendation systems
select recommendations by identifying salient keywords in retrieved documents,
or by querying an existing taxonomy or knowledge base for related concepts. In
this work, we build a text-to-text model capable of generating a collection of
recommendations directly, using the language model as a "soft" knowledge base
capable of proposing new concepts not found in an existing taxonomy or set of
retrieved documents. We train the model to generate recommendation sets which
optimize a cost function designed to encourage comprehensiveness,
interestingness, and non-redundancy. In thorough evaluations performed by crowd
workers, we confirm the generalizability of our approach and the high quality
of the generated recommendations.
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