Collaborative Filtering Recommendation Algorithm Based on Improved Similarity Computing

At presently the most widely used algorithm is collaborative filtering in the Personalized Recommendation Systems for E-Commerce. Aiming at the problem that the recommendation is not accurate due to the data sparsity, a collaborative filtering algorithm based on user characteristics and item attributes preference was proposed in this study. It obtained the nearest neighbor users and similar items by analyzing the user characteristics, item attributes and the data of user's historical scores, and then computing the similarity between the two users based on the user-based collaborative filtering algorithm. It gave an algorithm which has the lower value of MAE and may improve the accuracy of the recommendation services. Introduction With the rapid development of Internet technology, people’s lives have becoming more convenient. While at the same time, the problem of information overload is becoming serious. For e-commerce systems, the traditional search service model has been unable to meet the needs of accurate and efficient, so personalized recommendation systems for E-Commerce arising at the historic moment. Personalized recommendation services for E-Commerce can provide information and advice to customers so that to help customers deciding what products should to be bought, and then imitating sales people to help customers completing the purchase process [1]. The key task of the recommendation system is to recommend the related items according to the user interests analyzed from the relevant historical behavior of visitors. There are more current mainstream recommended methods such as Content-based recommendation, Collaborative Filtering recommendation, recommendation based on Association Rules and Hybrid recommendation. There are many famous recommendation examples such as Amazon, Jingdong, Taobao and movie which has used intelligent recommendation system to provide personalized recommendation service for users. Although the existing personalized recommendation technologies have achieved great success in various fields, but they also need to be improved for the accuracy of recommendation [2]. Now the Collaborative Filtering technology is the widely used technology, while it is also facing many questions, such as cold start, data sparsity and scalability. If these questions can be improved effectively, especially the recommended inaccuracy caused by the sparse data, it will have an important effort on personalized recommendation. Some researchers have been carried out on personalized recommendation related research, mainly includes the following research. Among the Content-based recommendation, the TF-IDF [3] (Term Frequency-Inverse Document Frequency, abbreviated TF-IDF) method proposed by Salton is commonly used. There are many famous systems based on content filtering system, such as the Massachusetts institute of technology Letizia [4] and newsgroups filtration system NewsWeeder [5] and others. In the 1992, the Collaboration Filtering was proposed by Goleberg et al. [6] and applied in the email recommendation system in the Tapestry. Since then, as a kind of filtering technology, based on reducing information overload, is widely used in the Internet [7]. The representative of the application of collaborative filtering recommendation technology is the GroupLens [8] system developed by the Minnesota state university GroupLens team. Collaboration Filtering is divided 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2015) © 2015. The authors Published by Atlantis Press 1375 into user-based and item-based two kinds. Its main idea is to analyze the user’s interest, find the specified users similar (interest) users in the user group, and get the target users neighboring collection. Then combine these similar user evaluations for information, and forming system for the specified user for this information to predict the degree of preference. Thereby the recommended [9] is generated. For the data sparsity problem in the Collaboration Filtering system, Lee using a pseudo scoring [10], Ahn and others used heuristics algorithms measure the similarity between users [11], Sarwar used of the singular value decomposition (SVD) clustering method to reduce the user-item rating matrix of dimension resulting in relatively dense data to solve the data sparsity problem [12]. Yuan proposed the collaborative filtering algorithm based on clustering [13], Liu proposed the collaborative filtering algorithm based on clustering of the attributes to predicted the score [14]. Agrawal put forward the Association Rules technology at first [15]. It is a recommendation algorithm based on data mining technology. In the study the improved collaborative filtering algorithm was approved and used. Aiming at the problem that the recommendation is not accurate due to the data sparsity, a collaborative filtering algorithm based on user characteristics and item attributes preference was studied. Improved Collaborative Filtering Algorithm Similarity computing based on user characteristics Web site generally in the registration requirements of the user was filled in gender, age, occupation, etc. the impacts of these factors on the recommendation in the calculation of similarity ware considered according to the use of the data set. (1) Sex characteristics Different gender of the users have different needs for the commodity, the similarity measure formula based on sex is: Sex(u,v)=1, u.sex=v.sex; Sex(u,v)=0, u.sex! =v.sex; (2) Age characteristics Users of different age groups have different needs, the similarity measure formula based on age is shown as: Age(u,v)=1, |u.age-v.age| 10. (3) Job characteristics the similarity measure formula based on job is listed as: Job(u,v)=1,u.job=v.job; Job(u,v)=0, u.job! =v.job. So the formula of the similarity based on user's characteristic was showed as Eq. 1. SimA(u,v)=a*Sex(u,v)+b*Age(u,v)+c*Job(u,v). (1) Among them, a, b, c is the percent of sex characteristics, age characteristics and job characteristics, and a+b+c=1. Similarity calculation based on the preference of item attributes The similarity calculation based on the preference of item attributes can help the recommendation system to find the implicit relationship among the sparse user evaluation data. The basic idea is that the degree of similarity between users is influenced by item's score and the degree of preference for a certain type of item. When two user evaluation the similar item, we considered that have high similarity between them. With I={I1,I2,. ,In} and P={P1,P2,. ,Pk}respectively present the item and attribute set, then the item-attribute matrix can be shown in Table 1.