A Personalized Collaborative Recommendation Algorithm Based on Hybrid Information

Abstract

Personalized recommendation systems can help people to find interesting things and they are widely used with the development of electronic commerce. Many recommendation systems employ the collaborative filtering technology, which has been proved to be one of the most successful techniques in recommender systems in recent years. With the gradual increase of users and items in electronic commerce systems, the time consuming nearest neighbor collaborative filtering search of the target customer in the total user space resulted in the failure of ensuring the real time requirement of recommender system. The paper proposed a personalized recommendation algorithm using hybrid information. User and item information is used for collaborative filtering to produce the recommendations. The recommendation joining user information and item information collaborative filtering is more scalable than the traditional one. Introduction The recommendation system in the electronic commerce website obtains the information from the customer, obtains the customer's interest, the hobby and the recommendation related commodity to the customer. Because the recommended system has great potential, so the research on the recommendation system is more and more. Collaborative filtering algorithm is one of the earliest and most successful recommendation techniques. At present, the algorithm is successful in both practice and research. However, with the deepening of the application and the wide range of applications, it also exposes some points, the most important is the following two points. The algorithm can be used in real time. Search for thousands of and target with similar interests, backgrounds of customers but the huge online customer groups, to find the speed of the neighbors to be improved, and thus improve the scalability of the algorithm. The recommended quality of the recommendation system is as easy to make two kinds of errors as other query system. However, the recommended system is not recommended for the mistake, that is, the customer does not like the item recommendation system is recommended. In electronic commerce, we should try to avoid this kind of mistake. Because, the error will cause the customer's dissatisfaction, thus destroys the recommendation system's authority. With the continuous expansion of the scale of electronic commerce system, the number of and the number of goods has increased dramatically, resulting in the calculation of the target user's nearest neighbor is not accurate, thereby reducing the system's recommendation quality. The paper proposed a personalized recommendation algorithm using hybrid information. User and item information is used for collaborative filtering to produce the recommendations. The recommendation joining user information and item information collaborative filtering is more scalable than the traditional one. 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2015) © 2015. The authors Published by Atlantis Press 1434 Problem analysis Collaborative filtering is the key problem to find the nearest neighbor, this step is the key to the whole process. And to find the best neighbor, we have to effectively build a good user project score matrix. On the other hand, as customers, the continuous growth of goods, resulting in the user rating data is extremely sparse. The results are not recommended for certain and thus affect the accuracy of the recommendation. Based on the product features, the collaborative filtering recommendation is proposed to construct the product file, and the potential of the user to a certain kind of product is based on the user's purchase quantity and the average purchase amount. Because of the construction of collaborative filtering user project matrix. It is generally based on the user's score for all projects, as shown in figure 1. Figure 1 An example For example, you can consider in this regard, the project will be divided into different categories. Users in the same category of projects are more similar than in different types of projects more accurate. And it can also reduce the data's high dimension. Finally, the user's interest in each part of the project is calculated with a certain heuristic rule to merge each class of recommendation results as the user's final recommendation. Personalized collaborative recommendation algorithm based on hybrid information 1. The model of the method Collaborative filtering is one of the methods used in e-commerce recommendation system. Its core idea is that users tend to buy the goods which have similar interests. The basic starting point is that the customer is able to classify the customers. These three points constitute the basis of collaborative filtering, the more the user interest in the product, the higher the evaluation, as shown in figure 2.