Voyageur: An Experiential Travel Search Engine

Evensen, Sara; Feng, Aaron; Halevy, Alon; Li, Jinfeng; Li, Vivian; Li, Yuliang; Liu, Huining; Mihaila, George A.; Morales, John; Nuno, Natalie; Pavlovic, Ekaterina; Tan, Wang-Chiew; Wang, Xiaolan

doi:10.1145/3308558.3314125

Cited by 7 publications

(7 citation statements)

References 6 publications

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“…We introduced subjective databases as a key enabling technology for supporting experiential search and built O DB, a first such system. O DB has also been used to power Voyageur, our experiential travel search engine [13]. O DB is based on a new data model that incorporates user-generated data into a database system that can support complex queries, but also gives the designer flexibility to tune the schema for the application needs.…”

Section: Resultsmentioning

confidence: 99%

Subjective databases

Li¹,

Feng²,

Li³

et al. 2019

Proc. VLDB Endow.

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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. Users should be able to pose queries that specify subjective experiences using their own words, in addition to conditions on the usual objective attributes. This paper introduces O DB, a subjective database system that addresses these challenges. We introduce a data model for subjective databases. We describe how O DB 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.

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Section: Resultsmentioning

confidence: 99%

Subjective databases

Li¹,

Feng²,

Li³

et al. 2019

Proc. VLDB Endow.

Self Cite

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“…Subjective Databases. Subjective data analysis is an emerging research field [25,39,41,56,63], allowing to mine and analyze user-generated data. Such data is widely used in web applications, online rating systems, and more generally in the social sciences [14,43].…”

Section: Related Workmentioning

confidence: 99%

Exploring Ratings in Subjective Databases

Amer-Yahia

Milo

Youngmann

2021

Proceedings of the 2021 International Conference on Management of Data

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Subjective data links people to content items and reflects who likes or dislikes what. The valuable information this data contains is virtually infinite and satisfies various information needs. Yet, as of today, dedicated tools to explore this data are lacking. In this paper, we develop a framework for Subjective Data Exploration (SDE). Our solution enables the joint exploration of items, people, and people's opinions on items, in a guided multi-step process where each step aggregates the most useful and diverse trends in the form of rating maps. Because of the large search space of possible rating maps, we leverage pruning strategies based on confidence intervals and multi-armed bandits. Our large-scale experiments with human subjects and real datasets, demonstrate the need for dedicated SDE frameworks and the effectiveness and efficiency of our approach. CCS CONCEPTS• Human-centered computing → Systems and tools for interaction design; Visualization systems and tools.

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“…Figure 3 makes these tasks explicit, where in addition to tagging, pairing, and sentiment classification, there is also the attribute classification task, which determines which attribute a pair of aspect and opinion terms belong to. Attributes are important for downstream applications such as summarization and query processing [10,22]. As we will describe, sentiment classification, pairing, and attribute classification are all instances of the span classification problem.…”

Section: Tagging and Span Classificationmentioning

confidence: 99%

“…Structured information, such as aspects, opinions, and sentiments, which are extracted from reviews are used to support a variety of real-world applications [1,10,17,22,25]. Mining such information is challenging and there has been extensive research on these topics [17,23], from document-level sentiment classification [24,61] to the more informative Aspect-Based Sentiment Analysis (ABSA) [32,33] or Targeted ABSA [38].…”

Section: Related Workmentioning

confidence: 99%

Snippext: Semi-supervised Opinion Mining with Augmented Data

Miao

Wang

et al. 2020

Proceedings of the Web Conference 2020

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Online services are interested in solutions to opinion mining, which is the problem of extracting aspects, opinions, and sentiments from text. One method to mine opinions is to leverage the recent success of pre-trained language models which can be fine-tuned to obtain highquality extractions from reviews. However, fine-tuning language models still requires a non-trivial amount of training data.In this paper, we study the problem of how to significantly reduce the amount of labeled training data required in fine-tuning language models for opinion mining. We describe Snippext, an opinion mining system developed over a language model that is fine-tuned through semi-supervised learning with augmented data. A novelty of Snippext is its clever use of a two-prong approach to achieve state-of-the-art (SOTA) performance with little labeled training data through: (1) data augmentation to automatically generate more labeled training data from existing ones, and (2) a semi-supervised learning technique to leverage the massive amount of unlabeled data in addition to the (limited amount of) labeled data. We show with extensive experiments that Snippext performs comparably and can even exceed previous SOTA results on several opinion mining tasks with only half the training data required. Furthermore, it achieves new SOTA results when all training data are leveraged. By comparison to a baseline pipeline, we found that Snippext extracts significantly more fine-grained opinions which enable new opportunities of downstream applications. ACM Reference Format:

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Voyageur: An Experiential Travel Search Engine

Cited by 7 publications

References 6 publications

Subjective databases

Subjective databases

Exploring Ratings in Subjective Databases

Snippext: Semi-supervised Opinion Mining with Augmented Data

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