2013 MultiSourceDeepLearningforInfor

• (Ge et al., 2013) ⇒ Liang Ge, Jing Gao, Xiaoyi Li, and Aidong Zhang. (2013). “Multi-source Deep Learning for Information Trustworthiness Estimation.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487612

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Multi-source+Deep+Learning+for+Information+Trustworthiness+Estimation
• http://dl.acm.org/citation.cfm?id=2487575.2487612&preflayout=flat#citedby

Quotes

Author Keywords

• Deep learning; information trustworthiness; miscellaneous; multiple-source

Abstract

In recent years, information trustworthiness has become a serious issue when user-generated contents prevail in our information world. In this paper, we investigate the important problem of estimating information trustworthiness from the perspective of correlating and comparing multiple data sources. To a certain extent, the consistency degree is an indicator of information reliability -- Information unanimously agreed by all the sources is more likely to be reliable. Based on this principle, we develop an effective computational approach to identify consistent information from multiple data sources. Particularly, we analyze vast amounts of information collected from multiple review platforms (multiple sources) in which people can rate and review the items they have purchased. The major challenge is that different platforms attract diverse sets of users, and thus information cannot be compared directly at the surface. However, latent reasons hidden in user ratings are mostly shared by multiple sources, and thus inconsistency about an item only appears when some source provides ratings deviating from the common latent reasons. Therefore, we propose a novel two-step procedure to calculate information consistency degrees for a set of items which are rated by multiple sets of users on different platforms. We first build a Multi-Source Deep Belief Network (MSDBN) to identify the common reasons hidden in multi-source rating data, and then calculate a consistency score for each item by comparing individual sources with the reconstructed data derived from the latent reasons. We conduct experiments on real user ratings collected from Orbitz, Priceline and TripAdvisor on all the hotels in Las Vegas and New York City. Experimental results demonstrate that the proposed approach successfully finds the hotels that receive inconsistent, and possibly unreliable, ratings.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 MultiSourceDeepLearningforInfor	Jing Gao Aidong Zhang Liang Ge Xiaoyi Li			Multi-source Deep Learning for Information Trustworthiness Estimation				10.1145/2487575.2487612		2013