Entity search based on consumer preferences leveraging user reviews

Entity search websites enable consumers to purchase products and entities. These websites commonly provide predefined search filters and contain entity information and user-created reviews. Consumers commonly exploit filters to eliminate irrelevant entities. Consequently, they explore reviews about the purified entities to identify an entity centered on their preferences. Inspired by this consumer-centric process, this paper introduces a mechanized model for entity search. In the presented model, consumer preferences are considered as queries comprising configurations for predefined filters and a textual part containing more explanations and constrains. Previous works on entity retrieval have not employed capabilities of structured entity information, and complete potential of insights in user reviews. In contrast, the presented model, considering this limitations, exploits structured entity information of predefined_slots to filter out many irrelevant entities. Consequently, it ranks entities by comparing text_part of consumer preferences with the entire core content of user reviews, indicating the order of corresponding entities centered on consumer preferences. The entity search model presented in this paper employs cutting-edge natural language processing (NLP) methods. Moreover, this paper introduces a model to curate entity search dataset, considering structured entity information and objective information in user reviews and exploit it to create a dataset by extracting and labeling restaurant information from Tripadvisor. The created entity search model incorporates a monoBERT-based text_ranker, fine-tuned employing the created training dataset, and evaluations indicate perceptible improvements in mean reciprocal rank (MRR), mean average precision (MAP), and normalized discounted cumulative gain (nDCG).