Collaborative Filtering Algorithm Based on Item Semantic and User Characteristics

摘要

The traditional Collaborative Filtering (CF) algorithm has the weakness of data sparseness and cold-start. Aiming at the problem, this paper presents a CF algorithm by combining item semantic and user characteristics. First, the method combines the similarity of user rating with user characteristics to get the user neighbors and calculate the user forecast rating. Meanwhile, it combines the similarity of item rating with item semantic to get the item neighbors and compute the item prediction rating. Then, the final recommendation is got by combining user forecast rating with item prediction rating. Experimental results show that this algorithm can alleviate data sparsity, by way of reducing cold-start problem and increasing prediction accuracy.