The rapid development of e-commerce websites and social networking applications has drastically increased the volumes of online generated data, leading to the term big data. With the rise in Internet population to 3.2 billion worldwide, on the average, 2.5 quintillion bytes of data is generated on daily basis [1]. Such greater volumes of data introduced information overload problem, when it is difficult to find the most relevant information from numerous diverse sources, e.g., websites, blogs, e-commerce, and social networking applications. The growing size of data has forced the research community to think beyond the simple search problem to the next level of filtering of pertinent information [2]. Past few years have seen significant progress in the development of powerful and intelligent tools to process and analyze the complex patterns in big data to extract the knowledge that is more meaningful for users. The potential ability to create intelligence from the analysis of raw data has been successfully applied to diverse areas, such as business, industry, sciences, social media, and e-commerce, to name a few. The ever-growing volume, complexity, and dynamicity of online information have necessitated the use of recommender systems as an appropriate tool for facilitating and accelerating the process of information engineering. The recommender systems apply numerous knowledge discovery techniques on users' historical and contextual data (e.g., location, time, preference, weather, device, and mood) to suggest information, products, and services that best match the user's preferences [3].

Chapter Contents:

• 1.1 Background
• 1.2 About the book
• Acknowledgments
• References

Inspec keywords: electronic commerce; social networking (online); recommender systems; Web sites

Other keywords: online generated data; social networking applications; e-commerce; Big Data; recommender systems; Websites

Subjects: Search engines; Information networks; Financial computing