Collaborative Filtering Recommendation Algorithm Based On Improved Trustworthiness

With the development of electronic commerce, it becomes more powerful everyday, personalized products recommendation as an important part, which has played an important role on promoting the development of e-commerce. One of the most important recommendation algorithms is the collaborative filtering algorithm (CF), which is applied to many e-commerce sites more or less. But in practical application, this recommendation algorithm also has its drawbacks. It can not give good service to consumers and companies, therefore, many scholars have proposed various improved algorithms.This paper analyzes the shortcomings of the use-based collaborative filtering algorithms, and proposes a new algorithm based on modified trustworthiness, and specified the application of this algorithm. Then we construct a framework for evaluation of algorithms: first, proposes concept models based on personalized products recommendation process; Secondly, being attempt to stand in the perspective of maximizing interests of the company to determine the company's main objectives and tasks, select the evaluating indicators in data collection module and recommended lists module by comparing existing indicators of the evaluation recommendations, according to the contribution of these indicators to achieve the main objectives and tasks, using AHP to ascertain the contribution rate and draw the conclusion of the evaluation results; in the running recommendation algorithm module, utilizing sensitivity analysis to analyze whether similarity calculation will affect the recommended results and how to affect, then to improve the algorithm further.Design experiment to prove whether the improved trustworthiness in the collaborative filtering. recommendation algorithms can get significant improvement the results. According to experiment results, the improved algorithm can significantly get better recommendation accuracy.