could you tell us a little more about this work. For example, the word positive may be a bad thing for a cancer survivor if the diagnosis is positive. Very interesting - look forward to following all of your different research paths in the future. Some words may have different sentiment in this context than usual. If the people you contact never reply, then these recommendations are of little use. … You are also working on other things - your work on sentiment influence in online social networks (developing a Good Samaritan Index for cancer survivor communities) has been well documented. My research focuses on business analytics and social computing, especially in the context of social networks and social media dating a penn scale. The idea is to recommend dating partners who a user will like and will like the user back. Q - What does the future of Machine Learning look like. com or Netflix) to accommodate the match of both taste and attractiveness. How to effectively integrate users personal profiles into recommendation to avoid cold start problems without hurting the method s generalizability is also an interesting question we want to address in future research. It is also possible to derive new social science theories from dynamic data through computational studies. I also enjoy several conferences related to social computing, such as SocialCom and SBP. In other words, a recommended partner should match a user s taste, as well as attractiveness. We did not use off-the-shelf word list because sentiment analysis should be specific to the context.
what question / problem were you trying to solve. I was involved in research projects that leveraged data from online social networks and social media. How to make ML algorithms as easy to use as MS Word and Excel. The accuracy rate of our classifier is close to 80%. A - People s behaviors in approaching and responding to others can provide valuable information about their taste, attractiveness, and unattractiveness. Now, let s discuss how this applies in some of your research. Q - What are the next steps / where else could this be applied. Editor Note - If you are interested in more detail behind the approach, both Forbes recent article and a feature in the MIT Technology Review are very insightful. :) You might also enjoy these interviews because you are awesome: %PDF-1. A - It really depends on the context and it is hard to find a silver bullet for all situations. ÉV YÍÁ¤,XÄWe recently caught up with Kang Zhao, Assistant Professor at the Management Sciences department, Tippie College of Business, the University of Iowa. The first would be on the algorithm side--better and more efficient algorithms for big data, as well as machine learning that mimics human intelligence at a deeper level. I usually try several methods and settle with the one with the best performance. A - I use JUNG, a Java framework for graph analysis, Mallet for topic modeling, lingpipe for text analysis, and Weka for data mining jobs. A - I usually keep an eye on journals such as IEEE Intelligent Systems, numerous IEEE and ACM Transactions, Decision Support Systems, among many others.
The model also considers the match of both taste and attractiveness when recommending dating partners. A - My first research project using a real-world dataset was about collecting and analyzing data about humanitarian agencies and their networks. I don t know the exact answer but I guess ML will develop along two directions.clue clue clueless clueless dating series.. Meanwhile, it is equally important to develop the right mindset--a data scientist needs to be able to come up with interesting and important ideas/questions when given some data. Q - What are your favorite tools / applications to work with. Q - Your recent work on developing a Netflix style algorithm for dating sites has received a lot of press coverage. Next, let s talk more about Machine Learning in Social Networks and Social Media. Here we directly measure one s influence, i. A - It is about the opportunity to do better prediction. Q - What question / problem were you trying to solve. Q - What was the first data set you remember working with dating a penn scale. His work applying Machine Learning to the world of online dating has generated significant coverage (Forbes, MIT Technology Review, UPI, among others), so we wanted to know more. We developed a sentiment classifier (using Adaboost) specifically for OHCs among cancer survivors. A - In short, we extended the classic collaborative filtering technique (commonly used in item recommendation for Amazon. .
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