Athens.
Up next at WebSci '09 is Patricia Victor, who begins by noting the growth in recommendation systems, including, for example, the advanced functionality on Amazon and in other e-commerce applications. Some 60% of Netflix users, for example, base their viewing on recommendations, and Netflix has offered a US$10m prize for an algorithm that improves its recommendation system by 10%.
There are two classes of recommendation systems: systems which are content-based and systems which are collaborative filtering-based. The latter focusses on similarities in the rating behaviour of users, and trust-based systems are often based on such algorithms. Epinions offers such a social trust network, and also allows users to evaluate other users by placing them in their network of trust, thereby conferring particular importance on these users' trust ratings. This also alleviates the 'cold start' problem with new users; it provides more reliable and accurate recommendations and leads to a kind of trust propagation through the network.