Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Random Walk Based Model Incorporating Social Information for
  Recommendations   Collaborative filtering (CF) is one of the most popular approaches to build a
recommendation system. In this paper, we propose a hybrid collaborative
filtering model based on a Makovian random walk to address the data sparsity
and cold start problems in recommendation systems. More precisely, we construct
a directed graph whose nodes consist of items and users, together with item
content, user profile and social network information. We incorporate user's
ratings into edge settings in the graph model. The model provides personalized
recommendations and predictions to individuals and groups. The proposed
algorithms are evaluated on MovieLens and Epinions datasets. Experimental
results show that the proposed methods perform well compared with other
graph-based methods, especially in the cold start case.

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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
3
The categories of this paper are: physics
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The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg