Network-based information filtering algorithms: ranking and recommendation
This work tackles information filtering problems for researchers and entrepreneurs in online platforms, but it appears incremental as it builds on existing network-based approaches without claiming major breakthroughs.
The paper addresses the challenge of filtering information from online user interactions to improve services like social networking and e-commerce, proposing network-based algorithms for ranking and recommendation without specifying concrete results or numbers.
After the Internet and the World Wide Web have become popular and widely-available, the electronically stored online interactions of individuals have fast emerged as a challenge for researchers and, perhaps even faster, as a source of valuable information for entrepreneurs. We now have detailed records of informal friendship relations in social networks, purchases on e-commerce sites, various sorts of information being sent from one user to another, online collections of web bookmarks, and many other data sets that allow us to pose questions that are of interest from both academical and commercial point of view. For example, which other users of a social network you might want to be friend with? Which other items you might be interested to purchase? Who are the most influential users in a network? Which web page you might want to visit next? All these questions are not only interesting per se but the answers to them may help entrepreneurs provide better service to their customers and, ultimately, increase their profits.