Abstract
Traditional collaborative filtering recommendation algorithm uses single dimensional data to calculate the similarity between users or items, ignoring the user’s preference, thus affect the recommendation accuracy. To this end, an averaging forecasting based multi-dimensional aggregation recommendation algorithm was proposed in this paper, which constructs the relationship aggregation function by user’s total score and dimension scores firstly, then apply the aggregation function to the initial multi-dimensional score that calculated by the modified averaging forecasting algorithm. The experiment result shows that compared with the previous collaborative filtering based recommendation algorithm, it has higher recommendation accuracy.
Highlights
The collaborative filtering algorithm is a kind of most commonly used algorithms in the recommender system
To fill this research gap, this paper proposes a MDAA (Multi-Dimension Aggregation recommendation based on Average forecasting) algorithm
In order to verify the performance of the MDAA algorithm, the experimental compares it with the traditional collaborative filtering recommendation algorithm on two data sets which are got from TripAdvisor
Summary
The collaborative filtering algorithm is a kind of most commonly used algorithms in the recommender system. The similarity measure is based on the assumption that users have only one score for each item [6, 7], which ignores user’s preference affects the accuracy of recommendation. To fill this research gap, this paper proposes a MDAA (Multi-Dimension Aggregation recommendation based on Average forecasting) algorithm. MDAA algorithm uses the multi-dimensional score to reduce the impact of data sparsity on recommendation. It adds the average measure to the traditional prediction algorithm, which reduces the impact of user preference on the recommendation. The algorithm can effectively improve the accuracy of the recommendation
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