Reducing the sparsity of contextual information for recommender systems

Abstract

Our work focuses on the improvement of the accuracy of context-aware recommender systems. Contextual information showed to be promising factor in recommender systems. However, pure context-based recommender systems can not outperform other approaches mainly due to high sparsity of contextual information. We propose an idea to improve accuracy of context based recommender systems by context inference. Context inference is based on effect discovered by analyses of the context as a factor influencing user needs. Analyses of the news readers reveals existence of behavioural correlation which is the main pillar of proposed context inference. Method for context inference is based on collaborative filtering and clustering of web usage (as a non-discretizing alternative to association rules mining).

Poster: Discovering Code Dependencies by Harnessing Developer's Activity

“…Many approaches attempt to reduce a space of task-related software artifacts, e.g., for bug fixing, but further research is required to uncover their direct dependencies and thus reduce the sparsity of an identified task context [7].…”