  The KNN approach, which is widely used in recommender systems because of its
efficiency, robustness and interpretability, is proposed for session-based
recommendation recently and outperforms recurrent neural network models. It
captures the most recent co-occurrence information of items by considering the
interaction time. However, it neglects the co-occurrence information of items
in the historical behavior which is interacted earlier and cannot discriminate
the impact of items and sessions with different popularity. Due to these
observations, this paper presents a new contextual KNN approach to address
these issues for session-based recommendation. Specifically, a diffusion-based
similarity method is proposed for considering the popularity of vertices in
session-item bipartite network, and a candidate selection method is proposed to
capture the items that are co-occurred with different historical clicked items
in the same session efficiently. Comprehensive experiments are conducted to
demonstrate the effectiveness of our KNN approach over the state-of-the-art KNN
approach for session-based recommendation on three benchmark datasets.
