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We examine whether aggregate daily Twitter keyword volumes over eight months from November 2011 to June 2012 can be used to predict aggregate daily consumer spending as reported by Gallup. We also examine whether Twitter keyword volume improves predictive ability over prediction based solely on current spending
Search engines usually return relevant sorted results based on the keywords. Because of the lack of considering the user's current search interest and intention, this kind of strategy may not meet users' personalized search requirements. In order to retrieve results associated with the user's current search interest
This paper describes a new exponential language model that decomposes the model parameters into one or more low-rank matrices that learn regularities in the training data and one or more sparse matrices that learn exceptions (e.g., keywords). The low-rank matrices induce continuous-space representations of words and
the industry. Much of what is available tells that information such as cookies and browsing history are used to target customers, associating keywords with specific groups of inventory. We designed and conducted a web-based shopping experiment with fifty participants to observe how people of different backgrounds and
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.