Studies in Computational Intelligence
2 primary works
Book 406
The textbook at hand aims to provide an introduction to the use of automated methods for gathering strategic competitive intelligence. Hereby, the text does not describe a singleton research discipline in its own right, such as machine learning or Web mining. It rather contemplates an application scenario, namely the gathering of knowledge that appears of paramount importance to organizations, e.g., companies and corporations.
To this end, the book first summarizes the range of research disciplines that contribute to addressing the issue, extracting from each those grains that are of utmost relevance to the depicted application scope. Moreover, the book presents systems that put these techniques to practical use (e.g., reputation monitoring platforms) and takes an inductive approach to define the gestalt of mining for competitive strategic intelligence by selecting major use cases that are laid out and explained in detail. These pieces form the first part of the book.
Each of those use cases is backed by a number of research papers, some of which are contained in its largely original version in the second part of the monograph.
Book 487
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes.
At the same time, a new evolution on the Web has started to take shape, commonly known as the "Web 2.0" or the "Social Web": Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people.
This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties - when used as proxies for interest similarity - are able to mitigate the recommenders' scalability problem.