Data sensitive recommendation based on community detection
2015
artykuł naukowy
angielski
- community detection
- collaborative filtering algorithm
- cold start
- predicted rating mechanism
EN Collaborative filtering is one of the most successful and widely used recommendation systems. A hybrid collaborative filtering method called data sensitive recommendation based on community detection (DSRCD) is proposed as a solution to cold start and data sparsity problems in CF. Data sensitive similarity is combined with Pearson similarity to calculate the similarity between users. α is the control parameter. A predicted rating mechanism is used to solve data sparsity problem and to obtain more accurate recommendation. Both user-user similarity and item-item similarity are considered in predicted rating mechanism. β is the control parameter. Moreover, in the constructed K-nearest neighbour set, both user-community similarity and user-user similarity are considered. The target user is either in the community or has some correlation to the community. Calculating the user-community similarity can cope with cold start problem. To calculate the recommendation, movielens data sets are used in the experiments. First, parameters αandβare tested and DSRCD is compared with traditional collaborative filtering recommendation algorithm (TCF) and Zhao’s algorithm. DSRCD always has better results than TCF. When K = 30, we have better performance results than Zhao’s algorithm.
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CC BY-NC-ND (uznanie autorstwa - użycie niekomercyjne - bez utworów zależnych)
publiczny
15