A Novel E-commerce Recommendation Algorithm based on Neural Network and Collaborative Analysis

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1 , pp A Novel E-commerce Recommendation Algorithm based on Neural Network and Collaborative Analysis Ning Wang 1,2,3, Qiaoling Zhang 4, Liejun Yang 5, Mingming Chen 6,7,8 1 Dept. of Information and Mechatronics Engineering, Xiamen Huaxia University, Xiamen, Fujian 2 Dept. of Automation, Xiamen University, Xiamen, Fujian 3 Fujian Province Engineering Research Center on New Generation of Information and Communication Technology and Wisdom Education 4 Dept. of Commerce, Xiamen Huaxia University, Xiamen, Fujian 5 Dept. of Computer Science, Ningde Teachers College 6 Dept. Of Information and Mechatronics Engineering, Xiamen Huaxia University, Xiamen, Fujian 7 Fujian Province Engineering Research Center on New Generation of Information and Communication Technology and Wisdom Education 8 University of Illinois at Springfield, United States Abstract. This paper proposes a novel E-commerce recommendation algorithm based on the neural network and collaborative analysis. With the rapid development of Internet, online shopping becomes one of the most important ways of shopping consumer. In the face of the shopping website product information, the amount of recommendation system to help users quickly searches for products that meet the requirements. The Internet rapidly expanding creates the personalized information recommendation service. This class provides personalized recommendation service system of intelligent algorithm are adopted to decrease the users' information overload that save for the user search costs, improve the quality of decision-making, enhance customer trust, satisfaction, loyalty to the businessman, even promote the purchase. Our research combines the neural network model to propose the new recommendation algorithm that will help to enhance the performance of the traditional methods which will be meaningful and innovative. Keywords: E-commerce, Recommendation Algorithm, Neural Network, Collaborative Analysis, Systematic Design and Implementation. 1 Introduction With the rapid development of Internet, online shopping becomes one of the most important ways of shopping consumer. In the face of the shopping website product information, the amount of recommendation system to help users quickly searches for products that meet the requirements. In numerous recommendation technologies, the application of collaborative filtering recommendation technology is more extensive. ISSN: ASTL Copyright 2016 SERSC

2 Different from the traditional information filtering technology of search engines, recommendation systems do not need to provide for search keywords, it will through the analysis of that the historical transaction records of the user or the user behavior mining potential interest, and to recommend, therefore, the recommendation system to meet the personalized requirements of users. Personalized recommendation system in e-commerce site has been widely used, and has brought the huge commercial value. According to the review, the methods could be summarized as the follows. Recommendation technology based on association rules. The technology is using association rules and find out the correlation between the transactions. If the rules of each interest group as the former, then rules after the piece is the recommendation of the interest group and must be the target. Based on the recommendations from the basic content filtering technology. Recommended based on content filter is the similarity between user interest and system resources calculation, while the relevance of the larger resources recommended to the user. Based on collaborative filtering recommendation technology. Collaborative filtering algorithm generally includes collaborative filtering recommendation algorithm based on memory and the collaborative filtering algorithm based on project. Memory-based collaborative filtering technology is also called collaborative filtering based on user. If the user a and user b close to the behavior of the similarity, we can recommend a user interested in resources to user b. Recommended in this paper, the existing evaluation index system for the review of the system, and the recommendation system evaluation index of the latest research progress were summarized, from the accuracy, variety, novelty, and shed light on the multiple perspectives such as coverage, and applicable to their respective advantages and disadvantages, and carried on the thorough analysis of the environment. In the later sections, we will propose our unique methodology and perspective on the issues. 2 The Characteristics of the Neural Network Associative memory is one of the important applications of artificial neural network. It is through the training sample set and makes sample network steady state as the associative memory is stored on the stable attractor. When those with incomplete or change the information of data, will enable the network to call stored sample pattern, make information is populated or correction which gives the correct result, this is the tolerance of memory neural network. In the formula one, we show the architecture. (1) Neural network fault tolerance reflects the network a kind of practical, because the network input the actual messages often there are incomplete or mutate, so the 100 Copyright 2016 SERSC

3 network has high fault tolerance. Hopfield neural network is showing its advantages in this aspect, in the sample attract much higher than the domain size forward neural network. When the neural network input with fault tolerance of the neural network can attract domain into chaos, chaos movement after a certain dynamic movement, can always reach the chaotic attractor, and finally to achieve correct discriminant fault categories. When the neural network input fault tolerance beyond a certain range, the network of the chaotic movement from the original attraction domain, and the final convergence is less than the original chaotic attractor as follows. (2) In the figure one, we show the sample architecture and organization. (3) Fig. 1. The Architecture and Organization of the Neural Network Structure 3 The Proposed Methodology Research on electronic commerce recommendation system to the enterprise and the society has the very high economic value. Personalized e-commerce recommendation system is the key to build the user model. Recommendation system recommendation algorithm and the technique of the hot issues are recommended. Recommendation algorithm is the core of the recommendation systems its performance determines the final recommendation result is good or bad. In order to establish a reasonable user model to meet different users, to the requirement of real-time and recommend ways to produce a series of recommended techniques and algorithms as includes technology based on the general content filtering technology, collaborative filtering technology and association rule mining, classification and clustering technology, neural network technology and so on. It is because there are many problems in general traditional Copyright 2016 SERSC 101

4 collaborative filtering recommendation algorithm and the researchers only constantly improved collaborative filtering recommendation algorithm is put forward. The combination of collaborative filtering and content-based algorithm. The combination of the two algorithms can make use of the advantages of the algorithm based on content, similarity matching of the project, especially when a project that has not been without the user's evaluation that can also recommend to the user. Collaborative filtering and combination algorithm based on association rules. Technology for collaborative filtering system is the use of association rules Apriori algorithm through mining the user's evaluation records related to that recommend. Based on the model of the algorithm only scans the database user ratings again can finish is recommended for all users. Advantage is to establish the model of much smaller compared to the original data set, so can effectively alleviate the real-time problem of recommendation algorithm. To make the whole system can operate effectively, correctly and that carefully to classify all kinds of different levels of awareness is to realize the important subject of collaborative e-commerce system. A sensory information may contain content has the following several aspects. Object: information about the user, such as online address, permissions, in a particular site registration data information, such as convenient to learn the user's state, this is the most basic part. Space: contains the user is (or was) browse the web site or web page. Work areas: in different circumstances, for the same value must be different, the degree of information such as a user engaged in advertising design for information about new mapping technology will pay more attention to than the other users. Time: this part describes whether the user is online and when once online. Focus: sometimes to perceive the different user requirements, such as a user opened several Windows at the same time, when he in dealing with other things do sometimes don't want to be too much interference and to inform the certain information only. The above information, the "object" is the most basic user information, different levels of general awareness information is composed of the cooperation. 102 Copyright 2016 SERSC

5 Fig. 2. The General Architecture of the Proposed Methodology 4 Conclusion This paper proposes a novel E-commerce recommendation algorithm based on the neural network and collaborative analysis. The Internet rapidly expanding creates the personalized information recommendation service. This class provides personalized recommendation service system of intelligent algorithm are adopted to decrease the users' information overload that save for the user search costs, improve the quality of decision-making, enhance customer trust, satisfaction, loyalty to the businessman, even promote the purchase. In the continuous improvement, although the personalized recommendation algorithm recommended accuracy has been greatly improved, but in practice, many users of personalized recommendation or the lack of adequate adopt motive exists negative cognition, in accepting that the recommendation service is not active merchants expected. For improving the traditional methodologies, we propose the new idea based on the NN which is novel. In the later time, we will test and verify the effectiveness of the feasibility of the proposed method. References 1. Yang, W.-S., Hwang, S.-Y.: itravel: A recommender system in mobile peer-to-peer environment. Journal of Systems and Software 86.1 (2013): Maity, M., Dass, M.: Consumer decision-making across modern and traditional channels: E-commerce, m-commerce, in-store. Decision Support Systems 61 (2014): McAuley, J., Pandey, R., Leskovec, J.: Inferring networks of substitutable and complementary products. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, Copyright 2016 SERSC 103

6 4. Mo, Y., Cloud-based mobile multimedia recommendation system with user behavior information. IEEE Systems Journal 8.1 (2014): Wu, D., Zhang, G., Lu, J.: A fuzzy preference tree-based recommender system for personalized business-to-business e-services. IEEE Transactions on Fuzzy Systems 23.1 (2015): Zhang, Y.: CADRE: Cloud-assisted drug recommendation service for online pharmacies. Mobile Networks and Applications 20.3 (2015): Ku, Y.-C., Tai, Y.-M.: What happens when recommendation system meets reputation system? The impact of recommendation information on purchase intention. System Sciences (HICSS), th Hawaii International Conference on. IEEE, Zhang, Y.: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, Cinicioglu, E.N., Shenoy, P.P.: A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores. Annals of Operations Research (2012): Copyright 2016 SERSC