Abstract：Hybrid recommender is a significant way for solving the defect of various single recommender methods.A hybrid recommender algorithm based on graph is proposed in this paper.Various recommended factors are fused into graph to produce the final recommendation results. The similarity between items is calculated using the content attribute of recommended items to build correlation matrix of the nearest graph. The item profile is constructed according to the scored record of item to generate a vector function. Grounded on the above, a regular framework is used to build a graph-based learning model by combining correlation matrix and vector function and realize a personalization recommendation based on graph. By the experiments on MovieLens datasets and transaction data of Amazon online mart, the effectiveness of the proposed algorithm is verified.
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