As for the low recommendation degree of the traditional collaborative filtering recommendation algorithm, a high efficient recommendation algorithm was put forward based on LDA. A new interest layer is added between the user layer and the project layer to produce the user - interest - item interest model. The interest information was extracted by expanded LDA, which makes use of the evaluation rank of the user on the project. Moreover, because of the constant change of the users' interests, pagerank algorithm is adopted, which takes account of the time characteristic, to support the random walk style to extend interests. Then items are recommended and user ratings are predicted according to the interest information. The experiments improve that the improved algorithm has better effect on recommendation by comparing with traditional SVD, LDA algorithms.
['Demin Hu', 'Linshan Chen']
A High Efficient Recommendation Algorithm Based on LDA