Summary
I found out the explanation of recommender system is a potential research subject in a few different areas. So I did a review for the relevant project within all the NSF funding award list. Here are some of my thoughts for each project.
- This project focuses on the exploration of the influence factors of recommendation and explanation on the user interaction in social media. Hence, the experiment is basically followed the existing functions in the social media services, e.g. Facebook and Twitter. This project implied the recommendation and explanation provided by social media did change the human behavior of online activity. There are always many criticisms of the social media "manipulation" the public opinion by its ranking algorithm. This concern makes this project is meaningful, due to the understanding of how the information affect user's preferences is still little known. According to the structure in my previous post, this is the first layer of the structure.
- CHS: Small: Context-aware mobile systems to facilitate synergistic face-to-face interactions (2014-2017)
- This project related to what we are doing now of the
people recommendation on CN3. I think it did require a mobile version of application to better answer the human face-to-face interaction. This would be the current trend to conduct the study. The project plans to conduct the experiment for a group of freshmen students. I think this would be a more stable user study setting for a long-term basis interaction pattern collecting.
- This project is very relevant to what I plan to do. They focus on the issues on sensitive data of user in the personalized system. First, they plan to distinguish the challenge for mobile user with sensitive contents. Second, develop a system with different personzied techniques to measure the prevalence. Third, identify the personalized political content. Fourth, personalized financial and health information applications. I think the PI and co-PI have a strong connection with commercial companies to gather necessary data set and user study environment. With a real world data set and system, it would be more make sense to claim the finding of privacy challenge and patterns. I wonder if there is a need for an interest-conflict-free study from my side? Say, a standalone small scale experiment to recall or further explain some of the issues can not be answered with the real world system or dataset.
- III: Small: Technologies for Creating Explanatory and Exploratory Animations from Scientific Data (2015-2018)
- This is a project for scientific data visualization, using an animation format. Our goal is not very relevant to this one, but the idea about how them formation the data into animation and make user easy to understand or use. This could bring us some insight about how to convert the recommender system result into a user-easy-understandable format.
- This project focuses on the decision support from machine learning, to answer how the interpretable of machine learning techniques can help the user to make a better decision. They focus on a well-known classifier - KNN as an example, to examine the interpretable in three metrics: simplicity, verifiability and accountability. The experiment is focused on how to make the classifier is intuitive to users, predictable and controllable. The similar idea can be also suitable for recommender system. But the problem is how to make the idea is novel and not just repeating the same idea from this project?
- A new direction of integrating human behavior to machine learning algorithm. This project is aimed to better facilitate the human behavior with the design of machine learning algorithm. There are some latest publications start to answer the issues and challenge in this area of "Humans in the Loop". This project is also focusing on the decision process and support from human. Furthermore, to design a better interface to connect the learning algorithm and human behavior. Ultimately, as a human interactive learning system. I am wondering the connection between human in the loop and the human in the "user modeling". In recommender study, we model the user based on their preferences and historical data, there is few studies discuss about how to let user to join and understand the process.
- I think this project answers my concern in "III: Medium: Machine Learning with Humans in the Loop". This project intends to develop an interface to extract users' high level knowledge, to a better user or data modeling. The visual interface can help to better analyze the human-machine interaction. As a long term grant, this implied some of the potential in this direction of research. But I think the visual analytic approach is only one of the way to engage user into the system. In some of the cases, for example, the text mining, the visual tool may not that useful. There may be some further potential work I can pursue in recommender system study.
- This project discussed more about the public awareness of the widely used algorithms. This is the latest funding since July 2016, which means, the state of the arts of the current researches. According to my literature review, this is right on the spot of the most potential research topic in this area. What I want to do on recommender system is pretty similar to this one. The main difference is that, I focus on more about the recommender system to item or people, but this project emphasizes more on the social media. However, I agree, based on the social media would be more suitable or simply to respond some of the interesting issues or channledge across disciplines. For instance, the law and ethics of the post ranking on Facebook or Twitter to affect the user political leanings. Even so, I think the recommender system can answer this question in a more genelize perspective, say, how the transparency and help answer the media bias. Also, the more potential issues in different area, e.g. e-commerce or location-based people system.
(*Rank by start-end funding year.)
