Economic transition policies in Chinese resource-based cities: An overview of government efforts

Although the area of Context-Aware Recommender Systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable “best bet” when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.

Comparing Context-Aware Recommender Systems in Terms of Accuracy andDiversity: Which Contextual Modeling, Pre-filtering and Post-FilteringMethods Perform the Best

We examine RecSys studies in China quantitatively, empirically, and longitudinally.Research on RecSys is relatively mature and well-developed overall.Twelve theme-clusters and six larger branches are identified.Some undeveloped or immature research themes continue to persist.Emerging themes with great potential for development are also identified. This paper examines the research patterns and trends of Recommendation System (RecSys) in China during the period of 2004-2013. Data (keywords in articles) was collected from the China Academic Journal Network Publishing Database (CAJD) and the China Science Periodical Database (CSPD). A co-word analysis was conducted to measure correlation among the extracted keywords. The cluster analysis and social network analysis revealed 12 theme-clusters, network characteristics (centrality and density) of the clusters, the strategic diagram, and the correlation network. The study results show that there are several important themes with a high correlation in Chinese RecSys research, which is considered to be relatively focused, mature, and well-developed overall. Some research themes have developed on a considerable scale, while others remain isolated and undeveloped. This study also identified a few emerging themes with great potential for development. It was also determined that studies overall on the applications of RecSys are increasing.

Research patterns and trends of Recommendation System in China using co-word analysis

In the circumstance of big data, the traditional collaborative filtering recommendation algorithm in e-commerce system is faced with the problems of data sparse, accuracy, real-time and etc., this paper proposes an improved clustering-based collaborative filtering recommendation algorithm. The algorithm introduces time decay function for preprocessing the user's rating and uses project attribute vectors to characterize projects, user interest vectors to users and use clustering algorithms to cluster the users and the projects respectively. Then the improved similarity measure methods are used to find the user's nearest neighbor and project recommended candidate set in the cluster. Finally, recommendations are produced. Theoretical analysis and experimental results show that the algorithm not only can effectively solve the problems of data sparse and new project, but also can portrait for users in multi dimension and reflect the user's interest changing. The recommended accuracy of the algorithm is improved obviously, too.

An improved clustering-based collaborative filtering recommendation algorithm

In a web environment, one of the most evolving application is those with recommendation system (RS). It is a subset of information filtering systems wherein, information about certain products or services or a person are categorized and are recommended for the concerned individual. Most of the authors designed collaborative movie recommendation system by using K-NN and K-means but due to a huge increase in movies and users quantity, the neighbour selection is getting more problematic. We propose a hybrid model based on movie recommender system which utilizes type division method and classified the types of the movie according to users which results reduce computation complexity. K-Means provides initial parameters to particle swarm optimization (PSO) so as to improve its performance. PSO provides initial seed and optimizes fuzzy c-means (FCM), for soft clustering of data items (users), instead of strict clustering behaviour in K-Means. For proposed model, we first adopted type division method to reduce the dense multidimensional data space. We looked up for techniques, which could give better results than K-Means and found FCM as the solution. Genetic algorithm (GA) has the limitation of unguided mutation. Hence, we used PSO. In this article experiment performed on Movielens dataset illustrated that the proposed model may deliver high performance related to veracity, and deliver more predictable and personalized recommendations. When compared to already existing methods and having 0.78 mean absolute error (MAE), our result is 3.503 % better with 0.75 as the MAE, showed that our approach gives improved results.

A collaborative recommender system enhanced with particle swarm optimization technique

Context-Aware Recommender Systems: Context-Aware Recommender Systems

Mobile Recommender Systems and Their Applications: Mobile Recommender Systems and Their Applications

The invention discloses a method and system for acquiring user preference in a mobile network. The method comprises the following steps: establishing a user preference acquisition model based on context computation so as to divide available context information into a current context and a historical context; dividing the context computation into current context-aware computation and historical context computation; taking user historical behavior and context thereof as main data sources, and accurately mining the user preference and change thereof step by step by utilizing methods such as collaborative filtering, historical context computation and the like; designing a user preference acquisition system in the mobile network according to the user preference acquisition model by combination with the actual situation of the mobile network so as to inspect the fitting effect of a user preference extraction model in the actual scene of the mobile network and further verify the availability and advanced property of the user preference extraction model; and finally achieving the purposes of extracting group user preference and individual user preference based on different objects from massive information.

Method and system for acquiring user preference in mobile network

The rapid growth of the number of mobile services provides enormous potential for users to find mobile information of interest. How to use both social network analysis and contextual recommendation techniques to improve mobile services recommendation has received little notice. In this paper we introduce a new heuristic approach to search nearest neighbors for each mobile user based on both given contextual mobile user preferences and mobile social network relationships, and then predict unknown contextual mobile user preferences and finally generate contextual mobile recommendations. We evaluate it on a real-world data set and compare its performance with some classical baseline methods, showing improvements in terms of different evaluation metrics.

http://www.aicit.org/JCIT/ppl/043%20Oct.%202011%20JCIT.pdf

Xiang-Wu Meng

Papers

Context-Aware Recommender Systems: Context-Aware Recommender Systems

Mobile Recommender Systems and Their Applications: Mobile Recommender Systems and Their Applications

Method and system for acquiring user preference in mobile network

A Heuristic Approach to Social Network-based and Context-aware Mobile Services Recommendation

Adaptive Learning Approach of Contextual Mobile User Preferences: Adaptive Learning Approach of Contextual Mobile User Preferences