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This paper presents a Slope one improved algorithm based on user similarity and user interest forgetting function. Aiming at the problem of large number of users and a lot of noise data, the inactive users are filtered out by setting the threshold of user activity, and then the neighbors of the target users are obtained through the calculation of user similarity. According to interest forgetting function, and then filter out items that have less effect on current users to reduce the noise data to improve the accuracy of the algorithm. Experimental comparison shows that the improved algorithm has better accuracy than the commonly used weighted Slope one and two-pole Slope one.

An Improved Slope One Algorithm Based on User Similarity Weights