A personalized hybrid recommendation strategy based on user behaviors and its application

Based on user's implicit feedback information, this paper puts forward an effective personalized recommendation algorithm. At first, a series of features are extracted from user behavior sequence, which are the input parameters of logistic regression model. Then we obtain a probability matrix. We view the obtained probability matrix as the scores, and use collaborative filtering recommendation strategy to recommend products to customers. The traditional collaborative filtering methods tend to ignore the impact of consumption time. Comparatively, this paper pays attention to the temporal behavior, which makes the personalized recommendation more reasonable. Our experimental results show that behavior sequence combined with collaborative filtering recommendation strategy has the ideal effect in recommendation. Besides, it has solved the problem that the strategy of collaborative filtering couldn't take advantage of implicit feedback directly. What's more, our algorithm performs well with sparse data. At last, beginning from the business features and the angel of statistics, this paper take some measures to adjust algorithm. Therefore, the result of the recommendation is optimized and the accuracy of the algorithm is improved.