ConRec: A Software Framework for Context-Aware Recommendation Based on Dynamic and Personalized Context

Contextual information is proven helpful to recommender system. And context-aware recommender system(CARS) has been applied in various applications. To improve the accuracy of context-aware recommendation and make recommender application development easier, we develop a lightweight software framework named ConRec, which introduces a dynamic context oriented approach to extend traditional reduction based recommender. This framework takes the dynamic nature of context into full consideration from different aspects to get better recommendation result. The dynamism of context exists in the process of context modeling, the computation of context weight and the handling of newly emergent context. In ConRec, context is dynamically modeled by clustering similar context values into one set automatically, rather than statically predefined by domain experts. Users´ preferences to different types of context are explicitly measured through context weighting function based on real dataset. Moreover, ConRec supports incrementally adding new type of context to recommendation process, which reduces much cost of re-building the whole recommender model. Based on our improved reduction-based algorithm, ConRec is built as a highly scalable and reusable software framework for developing context-aware recommender applications. Finally, we evaluate our proposed approach on public datasets and get more accurate recommendation than traditional methods.