Chia-Chi Wu

Collaborative filtering (CF) technology has been widely used in recommendation systems. Usually, the latent factor model (LFM) is used as the basis for implementing CF recommendation in deep learning systems. This study differs from previous studies in two respects. First, for different target items, the user embedding vector should be dynamically adjusted according to the content of the target item. Therefore, we have added an attention mechanism to dynamically adjust the user’s embedding vector. However, people’s preferences usually change over time. Therefore, based on the above attention model, this study considers two time-decay functions to emphasize the user’s recent preferences. The first decay function considers the situation where the recent rating is more important than the long ago rating. The second time-decay function considers the situation, whereby users generally prefer movies that have been released recently rather than movies that have been released a long time ago. By combining these two time-decay functions with the attention model, we propose a time-decay adaptive latent factor model (TDADLFM) model for item score prediction. This study applies this model to a dataset integrating Movielens-10M and HetRec2011 and proves that all three new considerations can improve recommendation performance.

This article describes how the bank industry in Taiwan must function in today's tough and fiercely competitive domestic credit card market and subdued global market. Banks are increasingly emphasizing the importance of retaining customers in order to sustain market share and remain profitable. This study proposes a new model which local banks can use to detect potential customer churn and provide an early warning indicator of problems that could lead to loss of customers. The model incorporates a customer relationship management database with a built-in time factor and applied temporal abstraction to represent data for a specific time period as defined by experts. Association rule mining is applied to analyze and detect abnormal customer behavior. The results of this article indicate that the system is relatively effective in detecting customer churn early on and thus helpful at assisting banks to address issues before they escalate. Furthermore, the tested rules are further scrutinized by experts to establish the relationship between the defined rules and management. This study provides an expert system for banks to assess the quality of their marketing campaigns and reestablish faltering customer relationships.

The rapid proliferation of social networking sites (SNSs) provides marketers with ample opportunities to explore advertising strategies based on electronic word-of-mouth (eWOM). With that in mind, this study aims to understand the influence of the eWOM diffusion process on SNSs. We propose a theoretical framework to investigate the roles of curiosity and influence in SNS users’ decisions to adopt eWOM and to pass it along to their contact groups. Two forms of curiosity, specific and diversive, are proposed to initiate specific exploration (consistency checking and knowledge-based validation) and diversive exploration (referral visit behavior), and eventually, adoption and pass-along behavior. In addition, SNS influence, formed by maven, persuasiveness, and connectivity, is proposed as a moderator affecting information diffusion. The proposed framework is empirically validated using the data collected from online surveys of 590 Facebook users. Implications for theory and practice are also discussed.