کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
383222 | 660808 | 2013 | 14 صفحه PDF | دانلود رایگان |
We present a flexible hybrid recommender system that can emulate collaborative-filtering, Content-based Filtering, context-aware recommendation, and combinations of any of these recommendation semantics. The recommendation problem is modeled as a problem of finding the most relevant nodes for a given set of query nodes on a heterogeneous graph. However, existing node ranking measures cannot fully exploit the semantics behind the different types of nodes and edges in a heterogeneous graph. To overcome the limitation, we present a novel random walk based node ranking measure, PathRank, by extending the Personalized PageRank algorithm. The proposed measure can produce node ranking results with varying semantics by discriminating the different paths on a heterogeneous graph. The experimental results show that our method can produce more diverse and effective recommendation results compared to existing approaches.
Journal: Expert Systems with Applications - Volume 40, Issue 2, 1 February 2013, Pages 684–697