Research On The Key Issues For The Recommender Systems

With the rapid development of Internet all around the world, the data and information on Internet has been increasing at a dramatical speed. Therefore, more customers are facing the problem of discovering the demanded contents from overwhelmingly massive data. As the result, this problem becomes a popular research topic and attracts attention from lots of scientists.Generally, there are three stages for users to maintain information from internet. First, various portal sites are established, such as sina, sohu, yahoo and so on. They help users filter and organize a variety of popular resource and information to discover and browse. However, the organized information is not always able to meet users’need, as well as overwhelming data will make the website overstaffed with the explosive growth of data, which results in the incompletion of information retrieval. Second, search engines start to emerge so that users are able to retrieve their desired contents, such as google and baidu. But the accuracy of search results quite depends on the description towards questions, which is usually not quite precise, thus the caused bias will make it difficult for users to identify exactly their required results. Third, recommender systems have been developed in recent years, which will intelligently recommend probably required information to users in conjunction with users’profile description and history record without users’ search operation. For instance, taobao and netflix will intelligently recommend items and movies to users, which can extract information for users when their requirement is not obvious enough. Noteworthily, the above three stages are not an evolution process, but a cooperative network instead.Recommender systems can properly deal with the information overload problem in internet, so they are widely welcome by users and thus adopted by great amount of websites and corporations. Therefore, recommend algorithms attract attention from academia and become a significant research area. However, with various kinds of data and complicated application environment, recommender systems will face different problems, for instance, normal problems like cold start and scalability; the difference in application environment and inconformity in data distribution will make the results from same algorithm differ from each other; new problems emerge as some recommend algorithms have trouble with calculation. In order to solve these problems, this paper intensively studies recommender system, and completes the following research work:(a) Similarity model research based on non-parametrical statisticsThe successfully applied collaborative filtering algorithms are the most fundamental and popular algorithms in recommender system research area. They consist of two steps, between which the calculation of similarity is the first and significant step. However, first, data under different application environment has individual characteristics and obvious difference in distribution, thus it is inaccurate to employ the same similarity measurement models; second, the traditional Euclidean distance, Pearson correlation and cosine similarity measurements are no longer suitable for complicated environment; third, the confidence probability is extremely small calculated from sparse data, the direct utilization of which will reduce the recommend accuracy. Because of the above reasons, this paper proposes a similarity model based on non-parametrical statistics, which is able to map data under different environments into a uniform space and standardize the data. Moreover, with the nice linearity in the projection space, similarity measurement is easy to calculate with aid of linear similarity, which solves the above problems and improves the recommend accuracy.(b) Demographic prediction with time backtrackingLack of data is always one of the biggest problem for various machine learning models, plenty of research work shows that data is far more significant than algorithms for the models. In recommender systems, the historical behaviors of users are the main source of model data. Traditional recommender systems can predict users’ profile like hobbits, ages and genders either by analyzing historical behaviors or by identifying similar users for recommendation. However, the employment of users’ historical behaviors used to be naive and simple, and ignores the time-varying property. Thus this paper proposes a time backtracking model, which promotes the utilization of historical data and increases data volume so as to improve the prediction accuracy. In addition, this paper applies this model into real word data from taobao to predict the age of users’children, and the experimental result shows the prediction accuracy is much higher than the traditional methods.(c) Evolutionary game theory inspired algorithm for global optimizationAmong the calculation process, lots of recommend algorithms and data mining problems will be transformed into solving the global optimization problem. Therefore global optimization problem is an important and challenging task in recommender systems. Currently, the frequently used algorithms, such as gradient descent method, stochastic gradient descent method and Newton method, are merely suitable for solving convex optimization problem. Thus this paper proposes an evolutionary game theory inspired algorithm to solve the global optimization problem in continuous domain without restraint of convex functions. Meanwhile, among the calculation process, a self-adapted parameter method is proposed to significantly improve the accuracy of algorithm and accelerate the converging speed to some extent.