Online users are always seeking experiences that fulfill various desires. Whether it’s a hotel with clean rooms, a lively bar, or a restaurant with a romantic ambiance, these searches play an integral role in nearly every aspect of travel. Unfortunately, e-commerce search engines do not support experiential queries. Even though text reviews often contain experiential data, users must rely on objective attributes like location, price, and cuisine to find the experience they are looking for.
Sentiment analysis and opinion mining techniques can be leveraged to extract relevant descriptions from text reviews. But a database system must be able to model subjective data and process queries in the user’s natural language to support cognitive and experiential search queries. It must also be capable of specifying predicates involving objective attributes.
We developed OpineDB, a subjective database system that addresses these challenges by interpreting subjective predicates against a database schema through a combination of natural language processing (NLP) and information retrieval (IR) techniques. In a conservative evaluation, OpineDB outperformed an IR-based search engine (IR) and an attribute-based query engine (AB) by up to 15% for hotel queries and 10% for restaurant queries. It also accelerated query processing by up to 660% without compromising on result quality.
We also built Voyageur, a front-end experiential search engine for travel, on top of OpineDB. Unlike traditional search engines, this application can handle subjective queries and combine them with objective attributes to elucidate more insights and tips for the user.