The traditional collaborative filtering algorithm only based on matrix produced by user access history to make recommendation and sparse data,and also cannot reflect the user’s interests timely, contrary to these problems, the personalized recommendation technology news in the traditional collaborative filtering algorithm proposes the calculation of news text content similarity and the concept of the time window , the calculation of news content similarity also takes into account the part of speech and positions of the feature words in the news, the time window is used to create user interest model which will change over time; The experimental results show that the improved algorithm effectively improves the sparse problem of data which user has accessed and captures user interest timely, F-measure value improves the maximum 10% compared to the traditional algorithm, the highest value of mean absoulte error fell by 7%, greatly improving the quality of recommendation.
