# Subjective Databases

## TL;DR

This paper presents Opine, a novel subjective database system that models and processes experiential user queries expressed in natural language, improving the retrieval of subjective data from reviews.

## Contribution

The paper introduces a data model and query processing techniques for subjective databases, enabling natural language experiential queries and effective ranking.

## Key findings

- Opine effectively matches user phrases to schema elements.
- Subjective databases outperform traditional methods in review data retrieval.
- Experiments demonstrate improved accuracy in experiential query results.

## Abstract

Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price, and cuisine, and any experiential data is relegated to text reviews. In order to support experiential queries, a database system needs to model subjective data and also be able to process queries where the user can express varied subjective experiences in words chosen by the user, in addition to specifying predicates involving objective attributes. This paper introduces Opine, a subjective database system that addresses these challenges. We introduce a data model for subjective databases. We describe how Opine translates subjective queries against the subjective database schema, which is done by matching the user query phrases to the underlying schema. We also show how the experiential conditions specified by the user can be combined and the results aggregated and ranked. We demonstrate that subjective databases satisfy user needs more effectively and accurately than alternative techniques through experiments with real data of hotel and restaurant reviews.