Estimating the Impact of California Tribal Gaming on Demand for Casino Gaming in Nevada

Since 1990, the California tribal casino industry has grown from a very small and insignificant industry to one with annual gross gaming revenues of about $7.5 billion per annum by 2009. Over this same period, Nevada\u27s gaming revenues grew from approximately$5.0 billion in 1990 to $10.4 billion in 2009, having declined from a peak of$12.8 billion in 2007. Much of the recent decline in Nevada and especially Las Vegas can be attributed to the severity of the economic recession of 2007-2009. However, the major Northern Nevada destination resorts of Reno and South Lake Tahoe had experienced substantial slowdowns or contraction of their gaming industries since the advent of California tribal gaming in the early 1990s, as measured in a number of ways, including number of gaming devices, employment, and gross gaming revenues adjusted for inflation. Las Vegas, on the other hand, had experienced substantial real growth over this same period, until the Great Recession of 2007-2009, at which point it experienced a dramatic reversal of fortune. This analysis estimates demand relationships for gaming activity in the major tourism markets in Northern and Southern Nevada, by specifying a number of variables that relate to the demand for gambling in these markets as well as noting monthly seasonal shifts. It also examines the competitive links between the expansion of California tribal gaming and the Nevada casino industry\u27s economic performance. Regression analysis is utilized to establish the relationship between the growth and expansion of tribal casinos in California and the expansion or contraction of gaming in Nevada\u27s major regions of Reno, Lake Tahoe, and the Las Vegas Strip

The design of a mood-driven chinese song recommendation system: Combining valence-based and polarity-based sentiment analysis on lyrics

Recommendation system (RS) can be lucrative in attracting and retaining users. A more intelligent RS would further make recommendations based on the user’s current mood. Driven by previous research that emotion is one of the dominating factors behind decision making, we present in this paper, an emotion-aware Chinese music RS which takes a user’s emotion state as an input to filter recommendation list accordingly. In particular, the system asks the users for a sentence describing their mood state then the system will try to recommend songs with the similar mood to the users. Such system can be used when lack of user data can be integrated into the music application to improve the overall user experience. Our system differentiates itself from most of the previous ones. In that the computational cost has been significantly reduced, thanks to the lightweight design of the core recommendation technique