Proposed Framework of Human-provided Services by David Schall
A more sophisticated way of integration is the Service-Oriented Architecture (SOA) consisting of services only. Despite of service compositions, services are independent and loosely coupled. They provide an interface and description about what the expected result is and how to use it.
Current implementations of SOA only focus on services implemented as software. But software services may lack some ability that humans do not. Recently, the Web 2.0 revolution demonstrats the power of people’s collaboration and collaborative creativity.
Vitalab, the distributed system group of the TU Wien, invented the so-called Human-provided Services (HpS). HpS perfectly fit into the SOA such that they may even combined with other services for service composition, orchestration and choreography. User need to formally describe their user profile and services they intend to provide. You think that this approach is nonsense? Some applications exist already. In some cases, people are still more effective than the most sophisticated systems. For instance, Amazon provides a crowd sourcing service. People are asked to categorize images as images with or without pornographic content. Current algorithm are far from the accuracy and efficency o human analysis of visual images. By the way, that’s the method how professional spamer cracked the CAPTCHA security mechanism. They told users to enter the CAPTCHA code in order to get some more pornographic content.
I appreciate the invention of HpS. Imagine SOA-based systems combining computational power with man power. The use of HpS in recommender systems is straight forward. Some systems like FilmTrust by Jennifer Golbeck may benefit since recommendations rely on trust inferencing in Social Networks. But some people, especially outliers of the Social Network are mostly people participating in other Web-based Social Networks. Using them as HpS would lead to a more complete base for providing recommendations. But that’s just potential use in recommender systems.
But one may think about combining content filtering with HpS as hybrid recommender system where people may integrate the semantic meaning content information and contextual awareness. Even collaborative filtering approaches may benefit from better recommendations, since people predict the interests of other people very easy and associate interesting products with the person.
In my case I’m playing a female fashionably druid, roaming forests and helping low skilled heros when they get attacked by wild animals. I’m able to transform into a fast panther, powerful bear, roaming gepard and of course a powerful Moonkin, a strange human beeing, looking like a bear going on two foots with deer antlers in an owl dress. Can you imagine? It looks quite funny ^^