Exploring Memory Cues to Aid Information Retrieval from Personal LifeLog Archives

The expansion of personal information archives and the emerging field of Personal Lifelogs (PLs) are creating new challenges for information retrieval (IR). While studies have demonstrated the difficulties of IR for these massive data collection [1], we should also think about how we can opportunities and benefits from integrating these data sources as a component of “digital memories” , considering their rich connections with the users‟ memory. We observed that most existing approaches to personal archive IR are mostly technology-driven. Although in recent years studies in Personal Information management (PIM) have claimed to make use of the human memory features, and many works have been reported as investigating well-remembered features of computer files (documents, email, photos). Yet, these explorations are usually confined to the attributes or feature that current computer file systems or technology have provided. I believe that there are important and potentially useful data attributes that these studies have ignored. In addition, current personal search interfaces provide searching options based on what is available in the system, e.g. require users to fill in the calendar date, regardless of the fact that people actually don‟t often encode „time‟ in such a way. My PhD project aims to explore what users actually tend to recall in different personal achieve information seeking tasks, how to present searching options to cater for the right type or format of information that users can recall, and how to exploit this information in an IR system for personal lifelog archives. In this paper, I discuss the limits and advantages of some related work, and present my current and proposed study, with an outlook of an interface that I plan to develop to explore my proposals

Auto Insurance Tenure Prediction and Analysis

The purpose of this project is to understand the main factors that drive customer tenure within auto insurance industry for six or more years. The analysis is based on three years of the J.D. Power Auto Insurance survey data. For the analysis, multiple binary machine learning algorithms were implemented and measured to classify whether customers would stay with the same insurer for more than six years. Random forest was found to be the most robust model as compared to logistic regression, decision trees, and xgboost