  Collaborative filtering (CF) is the most widely used and successful approach
for personalized service recommendations. Among the collaborative
recommendation approaches, neighborhood based approaches enjoy a huge amount of
popularity, due to their simplicity, justifiability, efficiency and stability.
Neighborhood based collaborative filtering approach finds K nearest neighbors
to an active user or K most similar rated items to the target item for
recommendation. Traditional similarity measures use ratings of co-rated items
to find similarity between a pair of users. Therefore, traditional similarity
measures cannot compute effective neighbors in sparse dataset. In this paper,
we propose a two-phase approach, which generates user-user and item-item
networks using traditional similarity measures in the first phase. In the
second phase, two hybrid approaches HB1, HB2, which utilize structural
similarity of both the network for finding K nearest neighbors and K most
similar items to a target items are introduced. To show effectiveness of the
measures, we compared performances of neighborhood based CFs using
state-of-the-art similarity measures with our proposed structural similarity
measures based CFs. Recommendation results on a set of real data show that
proposed measures based CFs outperform existing measures based CFs in various
evaluation metrics.
