Implementation And Evaluation Of Recommendation Algorithms Based On MAHOUT Technology

The emergence of the Internet makes electronic commerce the unprecedented development, which is associated with number of commodity and the types of explosive growth. In this context, the user in the multifarious information need to spend a lot of time and energy to find the goods they want, at the same time have their own unique needs of different users.Early for such problems, the traditional Internet companies have two solutions, one is to use catalog; Second is the Search Engine. But there is one common limitation for them:requires users to demand their own initiative. But sometimes users can’t quite describe their needs, in order to help users lock their interested items or information quickly and accurately, personalized recommendation system arises at the historic moment. The using of data mining technology, highly intelligent, able to provide users with personalized completely decision support and information services.To getting good experience for users, recommendation algorithm is of great importance is self-evident. Now commonly used in the actual production of recommendation algorithm for collaborative filtering recommendation algorithm, called collaborative filtering algorithm is take the user to select the essence goods, use of the relationship between the user and the commodity, analysis of users’interests, mining the similarity between different users or the similarity between different items, users are formed by a variety of different calculation methods for unknown goods preference degree of prediction.In this paper, the main work includes:1. The detailed elaborated the research background, development history and research status at home and abroad.2. The recommendation system related in detail in this paper, the main technology, which mainly analyzes the classification and evaluation standard of recommendation engine.3. The use of mahout collaborative filtering development model to realize the combination of several recommendation algorithm, and in the Amazon books data sets were evaluated, artificial validation and manual tuning of the algorithm.In this paper, the main innovation points:1. In the mahout deep insight into the collaborative filtering algorithm framework, on the basis of the result of the system implementation process and recommend tracking, analysis and evaluation of the results.2. Adopt the method of limited user attributes optimization recommendation process, make a recommendation results more reasonable and accurate.Based on Amazon’s book data settings on the combination of the realization of the algorithm show that in the case of the relatively small data sets, the recommended result is not ideal; By the method of limited user attributes can root mean square error of prediction results significantly decreases, and the algorithm is more stable. The practical application of the work of this paper to recommendation system has certain reference value.