Synergy: A Workbench for Collaborative Filtering Algorithms on User Interaction Data
Babar Khan Tareen · Seoul National University Open Repository (Seoul National University) · 2010
Collaborative Filtering (CF) is a process used by m any recommender systems for recommending content based on similarities between users. CF traditionally used i tem purchase history or rating information only, for fi nding out similar users. Nowadays users interact with the con tent in many different ways. Users can rate, tag, like, dis like or subscribe to the content and some information about relationship between users is also available usuall y in social network form. This additional interaction informati on can be used to filter out similar users more effectivel y. Various CF algorithms have been proposed to take into account this additional information and suggest better content. But it is very hard to figure out which algorithm will work b est on a particular dataset. In this paper, we propose a gen eric data model, for storing user interaction data, which can easily support many collaborative filtering algorithms. We have also implemented a tool which assists in creating, running and comparing different collaborative filtering alg orithms. The tool is based on the proposed data model and ca n easily be extended by using plugins. It is helpful in figu ring out which algorithm works best on a particular domain.