Many online retailers now offer consumer product ratings and reviews, and such information has become increasingly important to both retailers and consumers. However, extant empirical research shows mixed results regarding the effects of online ratings and reviews on product sales. We provide a theoretical framework to understand the impacts of consumer reviews and ratings on firms’ prices, sal...

Collaborative filtering allows the preferences of multiple users to be pooled in a principled way in order to make recommendations about products, services or information unseen by a specific user. We consider here the problem of online and interactive collaborative filtering: given the current ratings and recommendations associated with a user, what queries (new ratings) would most improve the...

Collaborative filtering (CF) allows the preferences of multiple users to be pooled to make recommendations regarding unseen products. We consider in this paper the problem of online and interactive CF: given the current ratings associated with a user, what queries (new ratings) would most improve the quality of the recommendations made? We cast this in terms of expected value of information (EV...

Online professional networks become more and more important for professional self-presentation, for recruitment processes, and job hunting. While previous studies determine that individuals present themselves authentically on rather privately used social networks, self-presentation on online professional networks is still unexplored. This study examines how validly individuals present themselve...

BACKGROUND AND OBJECTIVE The US public is increasingly using online rating sites to make decisions about a variety of consumer goods and services, including physicians. We sought to understand, within the context of other types of rating sites, parents' awareness, perceptions, and use of physician-rating sites for choosing primary care physicians for their children. METHODS This cross-section...

This paper presents a novel method that uses online ratings to predict business failures.

It is generally assumed that ratings are a numeric representation of text sentiments and their valences are consistent. This however may not always be true. Using a panel of data on over 4,000 books from Amazon.com, we develop a multiple equation model to examine the inter-relationships between ratings, sentiments, and sales. We find that ratings do not have a significant direct impact on sales...

The problem of unfair testimonies remains an open issue in reputation systems for online trading communities. A common attempt is to use binary ratings to model sellers’ reputation. However, this attempt leads to that the research of tackling unfair testimonies also focuses on reputation systems using binary ratings. In this extended abstract, we propose a two-stage clustering approach to filte...