Cai, Y, Pan, S, Wang, X, Chen, H, Cai, X & Zuo, M 2020, 'Measuring distance-based semantic similarity using meronymy and hyponymy relations', Neural Computing and Applications.View/Download from: Publisher's site
© 2018, The Natural Computing Applications Forum. The assessment of semantic similarity between lexical terms plays a critical part in semantic-oriented applications for natural language processing and cognitive science. The optimization of calculation models is still a challenging issue for improving the performance of similarity measurement. In this paper, we investigate WordNet-based measures including distance-based, information-based, feature-based and hybrid. Among them, the distance-based measures are considered to have the lowest computational complexity due to simple distance calculation. However, most of existing works ignore the meronymy relation between concepts and the non-uniformity of path distances caused by various semantic relations, in which path distances are simply determined by conceptual hyponymy relation. To solve this problem, we propose a novel model to calculate the path distance between concepts, and also propose a similarity measure which nonlinearly transforms the distance to semantic similarity. In the proposed model, we assign different weights in accordance with various relations to edges that link different concepts. On basis of the distance model, we use five structure properties of WordNet for similarity measurement, which consist of multiple meanings, multiple inheritance, link type, depth and local density. Our similarity measure is compared against state-of-the-art WordNet-based measures on M&C dataset, R&G dataset and WS-353 dataset. According to experiment results, the proposed measure in this work outperforms others in terms of both Pearson and Spearman correlation coefficients, which indicates the effectiveness of our distance model. Besides, we construct six additional benchmarks to prove that the proposed measure maintains stable performance.
Xiong, F, Wang, X, Pan, S, Yang, H, Wang, H & Zhang, C 2020, 'Social Recommendation With Evolutionary Opinion Dynamics', IEEE Transactions on Systems Man and Cybernetics: Systems.View/Download from: Publisher's site
IEEE When users in online social networks make a decision, they are often affected by their neighbors. Social recommendation models utilize social information to reveal the impact of neighbors on user preferences, and this impact is often described by the linear superposition of neighbor preferences or by global trust propagation. Further exploration needs to be undertaken to determine whether the influence pattern of other users from online interaction behaviors is adequately described. In this paper, we introduce evolutionary opinion dynamics from the field of statistical physics into recommender systems, characterizing the impact of other users. We propose an opinion dynamic model by evolutionary game theory. To describe online user interactions, we define the strategies during an interaction between two users, and present the payoff for each strategy in terms of errors of estimated ratings. Therefore, user behaviors are associated with their preferences and ratings. In addition, we measure user influence according to their topological roles in the social network. We incorporate evolutionary opinion dynamics and user influence into the recommendation framework for the prediction of unknown ratings. Experiment results on two real-world datasets demonstrate that our method outperforms state-of the-art models in terms of accuracy, and it also performs well for cold-start users. Our method reduces the divergence of user preferences, in accordance with online opinion interactions. Furthermore, our method has approximate computational complexity with matrix factorization, and results in less computation than state-of-the-art models. Our method is quite general, and indicates that studies in social physics, statistics, and other research fields may be involved in recommendation to improve the performance.
Users in online networks exert different influence during the process of information propagation, and the heterogeneous influence may contribute to personalized recommendations. In this paper, we analyse the topology of social networks to investigate users’ influence strength on their neighbours. We also exploit the user-item rating matrix to find the importance of users’ ratings and determine their influence on entire social networks. Based on the local influence between users and global influence over the whole network, we propose a recommendation method with indirect interactions that makes adequate use of users’ relationships on social networks and users’ rating data. The two kinds of influence are incorporated into a matrix factorization framework. We also consider indirect interactions between users who do not have direct links with each other. Experimental results on two real-world datasets demonstrate that our proposed framework performs better than other state-of-the-art methods for all users and cold-start users. Compared with node degrees, betweenness, and clustering coefficients, coreness constitutes the best topological descriptor to identify users’ local influence, and recommendations with the measure of coreness outperform other descriptors of user influence.
Wang, X, Liu, Y, Lu, J, Xiong, F & Zhang, G 2019, 'TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators', FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, vol. 94, pp. 224-236.View/Download from: Publisher's site
© 2013 IEEE. With the development of the mobile phone industry, mobile applications market becomes thriving. However, the immature information technology and unfriendly interface bring negative user experience (UX) to the mobile users and thus affect the service life of mobile applications, especially for the most concerned entertaining applications, mobile games. As a result, the evaluating UX and finding crucial factors of UX become a challenge. Over the last decades, numerous researches have tried to deal with this issue, but none of them has clearly identified the relations among positive-negative UX, sufficient human characteristics and specific events of applications. This paper proposes a subjective-objective evaluation method. Subjective UX of mobile games is sufficiently obtained and objective UX is verified through the electrocardiogram signals and heart rate variability. In order to reveal distinct relations among UX factors, sufficient user characteristics and categories of game events, an improved RIPPER algorithm is proposed by this paper to obtain the relations. Experiments are performed with 300 testers who played mobile parkour games for at least five minutes. The accuracy and efficiency of the proposed method have been verified through real experiments and objective measures. In addition, this paper provides an effective sampling method and a data analysis algorithm to obtain crucial UX factors for mobile applications.
Wang, X, Liu, Y, Zhang, G, Xiong, F & Lu, J 2017, 'Diffusion-based recommendation with trust relations on tripartite graphs', JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT.View/Download from: Publisher's site
Wang, X, Liu, Y, Zhang, G, Zhang, Y, Chen, H & Lu, J 2017, 'Mixed Similarity Diffusion for Recommendation on Bipartite Networks', IEEE Access, vol. 5, pp. 21029-21038.View/Download from: Publisher's site
© 2013 IEEE. In recommender systems, collaborative filtering technology is an important method to evaluate user preference through exploiting user feedback data, and has been widely used in industrial areas. Diffusion-based recommendation algorithms inspired by diffusion phenomenon in physical dynamics are a crucial branch of collaborative filtering technology, which use a bipartite network to represent collection behaviors between users and items. However, diffusion-based recommendation algorithms calculate the similarity between users and make recommendations by only considering implicit feedback but neglecting the benefits from explicit feedback data, which would be a significant feature in recommender systems. This paper proposes a mixed similarity diffusion model to integrate both explicit feedback and implicit feedback. First, cosine similarity between users is calculated by explicit feedback, and we integrate it with resource-allocation index calculated by implicit feedback. We further improve the performance of the mixed similarity diffusion model by considering the degrees of users and items at the same time in diffusion processes. Some sophisticated experiments are designed to evaluate our proposed method on three real-world data sets. Experimental results indicate that recommendations given by the mixed similarity diffusion perform better on both the accuracy and the diversity than that of most state-of-the-art algorithms.