Abstract:

With the continuous expansion of Internet information, the Internet has entered the big data era.The recommendation systems have come into being to solve the problem of information overload. The core of recommendation system is the recommendation algorithm. The slope one algorithm is a simple, efficient and typical collaborative filtering recommendation algorithm. This algorithm uses the linear regression of user-item score matrix to predict the scores of items. The data sparsity is the major problem affecting its accuracy because the matrix is usually very sparse. In this paper, we propose an improved slope one algorithm to solve this problem. Firstly, the similarity between items is calculated and is added to the score formula. Then, we use single value decomposition to reduce dimension of the sparse user-item value matrix and generate a similar but denser matrix to be the new input of slope one algorithm. Item similarity takes the internal relation into consideration and makes the result more reasonable. Single value decomposition converts the sparse matrix to a more appropriate form for calculation. Through the mix of the two techniques, the new algorithm has better prediction accuracy and flexibility.

Key words: collaborative filtering, slope one, item similarity, single value decomposition