GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
A graph model for E-commerce recommender systems
Journal of the American Society for Information Science and Technology
Comparing Recommendation Strategies in a Commercial Context
IEEE Intelligent Systems
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
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Recommendation in education portal is helpful for students to know the important learning resources in schools. Currently, previous methods which have been proposed to solve this problem mainly focus on page view counts. A learning resource is important just because many students have viewed it. However, as the metadata in a resource is becoming available, the relations among the resources and other entities in real world are becoming more and more. Unfortunately, how to use such relations to make better recommendations has not been well studied. In this paper, we present a complementary study to this problem. Specially, we focus on a general education portal, which consists of different typed objects, including resource, category, tag, user and department. The recommendation object is resource. However, we have found that a resource's importance rank can be affected by its relations to other typed objects. Thus, we formalize the resource recommendation as a ranking problem by considering its relations to other typed objects. A random walk algorithm to estimate the importance of each object in the education portal is proposed. Finally, the experimental result is evaluated in a real world data set.