A collaborative filtering recommendation algorithm based on improved similarity measure method

Collaborative filtering recommendation algorithm is one of the most successful technologies in the e-commerce recommendation system. With the development of e-commerce, the magnitudes of users and commodities grow rapidly; the performance of traditional recommendation algorithm is getting worse. So propose a new similarity measure method, automatically generate weighting factor to combine dynamically item attribute similarity and score similarity, form a reasonable item similarity, which bring the nearest neighbors of item, and predict the item´s rating to recommend. The experimental results show the algorithm enhance the steady and precision of recommendation, solve cold start issue.