CHI has provisionally accepted our first paper on context-aware peer-to-peer transaction partner matching. In this research, we focused on mining timebankers’ profiles for contextual information, namely their bios, offers and requests. We used this information to match them to suitable partners with common interests and complementary abilities and needs. The paper will be presented in San Jose, May 7-12:
https://chi2016.acm.org/wp/
Here are the title and abstract:
‘MASTerful’ Matchmaking in Service Transactions: Inferred Abilities, Needs and Interests versus Activity Histories
Timebanking is a growing type of peer-to-peer service exchange, but is hampered by the effort of finding good transaction partners. We seek to reduce this effort by using a Matching Algorithm for Service Transactions (MAST). MAST matches transaction partners in terms of similarity of interests and complementarity of abilities and needs. We present an experiment involving data and participants from a real timebanking network, that evaluates the acceptability of MAST, and shows that such an algorithm can retrieve matches that are subjectively better than matches based on matching the category of people’s historical offers or requests to the category of a current transaction request.