In the recommended process,the traditional algorithm has several problems. For examples,the algorithm ignored the customer preference and the customer＇s malevolence,also,it provided a variety of false information,even for time series problems. This paper introduced users＇ interests modeling while it built the model. Then,it toke about users＇ suspicious degree and time effects used to calculate and updated users＇ similarity. In the end,it would highlight the optimum recommended target. In order to avoid data overfitting,it used the greedy al...
