Yezheng Liu

Maximizing customer satisfaction through an online recommendation system: A novel associative classification model

Offering online personalized recommendation services helps improve customer satisfaction. Conventionally, a recommendation system is considered as a success if clients purchase the recommended products. However, the act of purchasing itself does not guarantee satisfaction and a truly successful recommendation system should be one that maximizes the customers after-use gratification. By employing an innovative associative classification method, we are able to predict a customers ultimate pleasure. Based on customers characteristics, a product will be recommended to the potential buyer if our model predicts his/her satisfaction level will be high. The feasibility of the proposed recommendation system is validated through laptop Inspiron 1525.

CSMC: A combination strategy for multi-class classification based on multiple association rules

Constructing accurate classifier based on association rules is an important and challenging task in data mining and knowledge discovery. In this paper, a novel combination strategy for multi-class classification (CSMC) based on multiple rules is proposed. In CSMC, rules are regarded as classification experts, after the calculation of the basic probability assignments (bpa) and evidence weights, Yangs rule of combination is employed to combine the distinct evidence bodies to realize an aggregate classification. A numerical example is shown to highlight the procedure of the proposed method at the end of this paper. The comparison with popular methods like CBA, C4.5, RIPPER and MCAR indicates that CSMC is a competitive method for classification based on association rule.

Online pricing with bundling and coupon discounts

We propose an online pricing strategy by utilising product bundling and coupon discounts. Given customer’s purchase behaviour and preference for bundling and coupon, we propose a nonlinear mixed-integer programming model to determine the most appropriate bundle discount and instant coupon so as to maximise e-tailer’s profit. A fast heuristic algorithm is designed to implement the proposed model online in real time. We investigate the robustness of the proposed method by examining how uncertainties in system parameters affect performance. Through collaborative optimisation, we offer important insights and managerial implications, and show how marketers can attract more purchase and maximise profit by properly integrating marketing tools such as bundling and coupon.

PT-LDA

Online social network presents a great opportunity to analyze user behavior and mine the implicit personality traits from the social network data. Considering the personality recognition as a multi-label classification problem, this paper proposes a new probabilistic topic model (PT-LDA model) to predict the personality traits within the framework of Five Factor Model. The proposed model extends the Latent Dirichlet Allocation (LDA) model to integrate the n-gram features into few latent topics and each topic is characterized by not only the multinomial distribution over words but also the Gaussian distributions over personality traits. This paper develops a Gibbs-EM algorithm to solve the proposed model iteratively based on Gibbs sampling and expectation maximization. Quantitative evaluation shows that PT-LDA is more accurate, efficient and robust than several baselines. Our experiment also shows that the proposed model can be used to extract the interpretable topics associated with each personality trait, which provides a new way to uncover user behaviors in online social network.

Identifying social influence in complex networks

Identifying influential peers is an important issue for business to promote commercial strategies in social networks. This paper proposes a conductance eigenvector centrality (CEC) model to measure peer influence in the complex social network. The CEC model considers the social network as a conductance network and constructs methods to calculate the conductance matrix of the network. By a novel random walk mechanism, the CEC model obtains stable CEC values which measure the peer influence in the network. The experiments show that the CEC model can achieve robust performance in identifying peer influence. It outperforms the benchmark algorithms and obtains excellent outcomes when the network has high clustering coefficient.

Research on the measure method of complaint theme influence on online social network

Abstract Consumer complaints on online social network quickly become online groups complaints through many people’s aggregation and looking on, interaction and word-of-mouth communication. Therefore, assessing and managing online complain influence has become a new problem for enterprise to listen to and manage online group complaints. This paper analyzed the complaint information feature of consumer group on online social network, from three-dimensional perspective of complaint text’s quality, transmission timeliness and user interaction degree. We built the influence measure model of online complaint theme based on entropy weight model by monitoring and analyzing real-time the static and dynamic properties of complaint information, explored the measure method of complaint theme influence, employed empirical method to verify the validity and provided scientific decision-making tools and methods for enterprise listening to and managing online group complains.

Integrating classification capability and reliability in associative classification: A β-stronger model

Mining class association rules is an important task for associative classification and plays a key role in rule-based decision support systems. Most of the existing methods try the best to mine rules with high reliability but ignore their capability for classifying potential objects. This paper defines a concept of @b-stronger relationship, and proposes a new method that integrates classification capability and classification reliability in rule discovery. The method takes advantage of rough classification method to generate frequent items and rules, and calculate their support and confidence degrees. We propose two new theorems to prune redundant frequent items and a concept of indiscernibility relationship between rules to prune redundant rules. The pruning theorems afford the associative classifier with good classification capability. The experiment shows that the proposed method generates a smaller frequent item set and significantly enhances the classification performance.

Collaborative matrix factorization mechanism for group recommendation in big data-based library systems

Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups.,The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed.,Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment.,The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts.,The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient.,The proposed methods have potential value to improve scientific collaboration and research innovation.,The proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.

A crowdsourcing-based topic model for service matchmaking in Internet of Things

Abstract The Internet of Things (IoT) provide intelligence for the communication between people and physical objects. An important and critical issue in the IoT service applications is how to match the suitable IoT services with service requests. To solve this problem, researchers use semantic modeling methods to make service matching. Semantic modeling methods in IoT extract meta-data from text using rule-based approaches or machine learning techniques often suffer from the scalability and sparseness since text provided by sensors is short and unstructured. In recent years, topic modeling has been used in IoT service matchmaking. However, most topic modeling methods do not perform well in IoT service matchmaking since the text is too short. In order to address the issues, this paper proposes a new topic modeling method to extract topic signatures provided by intelligent devices. The method extends the classical knowledge representation framework and improves the qualities of service information extraction, and this process is able to improve the effectiveness of service matchmaking in IoT service. The framework incorporates human cognition to improve the effectiveness of the algorithm and make the algorithm more robust in heterogeneous systems in the IoT. The usefulness of the method is illustrated via experiments using real datasets.

Optimizing online recurring promotions for dual-channel retailers: Segmented markets with multiple objectives

Abstract Online promotion helps enhance brand awareness and boost sales. Although it attracts customer traffic, an ill-conceived price promotion has serious repercussions because it disproportionately draws bargain hunters, results in profit erosion and causes operational chaos due to erratic demands. This research proposes a long-term optimization model to help dual channel (click-and-mortar) retailers understand the conditions necessary to promote products online across all markets. When partial markets are recommended, we investigate how to price and select the market portfolio for promotion in each time period. We develop a multi-objective evolutionary algorithm to efficiently solve complex and large-scale problems. Both theoretical analysis and numerical study show that the proposed model outperforms the conventional strategy of promoting online across the board. Due to its dynamic nature, the multi-period recurring promotion problem is difficult to address optimally. Our model is capable of planning for multiple periods, multiple markets, and multiple objectives to maximize long term profitability and competitiveness. Click-and-mortar retailers will find our approach extremely effective for maximizing profit, enhancing brand awareness, and improving customer satisfaction.