Dr. Huang Arrival
Dr. Huang, my advisor from Florida arrived in Beijing a few days ago. She is originally from China an came back for about a month to see family, attend a conference, and of course she squeezed in a little time Adriana, Jake and myself. We spent a few days in various meetings, updating her on our progress and showing her where we wanted to go from that point.
For my project in-particular, Dr. Zhang, Dennis and myself felt that we did not have enough time to complete our proposed research. The three of us and Dr. Huang together found a way to narrow the problem space of the project.
The narrowed down problem space would focus on the similarity function, the first step in the recommender process, and on hybrid recommenders that utilized tag information.
After the project work was scheduled out for the next few weeks, Dr. Huang took us to BeiHang University. We met with some of her friends who are professor at BeiHang University. The main goal of the meeting was to get “the ball rolling” so that next year students in the PIRE program could also perform research at BeiHang University and Tsinghua University.
Tian Jin
On August 2nd, Liu a student that Jake has been working with took us to Tian Jin. The city is about 150 kilometers from Beijing, we took a bullet train to get there, traveling at 330 km/hr.
Many of the chinese that we have been working with at Tsinghua University told us that we had to try a special type of dumpling in Tian Jin, called Go Bu Li (Dog’s Don’t Care). Don’t worry the dumplings were not filled with dog, its just a name. The dumplings were wonderful, the second best we had our whole trip, second only to the dumplings in Xi’an.
We only spent a few hours in Tian Jin, but it was a great experience, it was defiantly the place to go and try a type of Chinese food. The food in Tian Jin was sweeter than the food in Beijing, which was a nice difference. I definitely recommend spending a day in Tian Jin to the students who go to Tsinghua University next year.
Words of Wisdom/Suggestions
Buy a bike ~¥120-200
Get a multipass/multiuse public transportation card at any subway station
Be ready for spicy food
Be ready of cold food being almost nonexistent
lots of bargaining
Helpful Websites http://www.atthewu.com/ http://chinabites.com/beijing/haidian/wudaokou/ http://www.thebeijinger.com/newsletter http://www.tours.bj.cn/
Laundry in building 18
Take some Mandarin lessons, it really helps
Bus 731 is right outside the foreign dorms at Tsinghua University, one stop will take you to Wu Dao Kou
Things I loved about China
The Food - its nothing like the Chinese food we have in America
The Summer Palace
The Great Wall - Mu Tan Yu
Public Transportation - Cheap/Get Anywhere
Things I didn’t expect about China
Lack of paper towels/napkins
The pollution wasn’t as bad as I thought it was going to be
Kindness/Giving nature of the chinese people
Diversity of food/people/languages
Everything is huge and colorful
Things to pack (that are hard to come by in China)
Peanut butter
Oat meal
Gatorade powered
Sites To See in Beijing and Xi’an
Beijing
The Summer Palace
The Great Wall - Recommend Mu Tan Yu area
The Temple of Heaven
The Forbidden City
Hou Hai
Beijing Zoo
Beijing Aquarium
Beijing Planetarium
SIlk Street
Beijing Acrobat Show
Food Market - anything on a stick
Tian’amen Square
Mao’s Tomb
National Museum of the People
Computer Market
Olympic Green/Birds Nest/Water Cube
Xi’an (By Over Night Train Z19/Z20)
Terra-cotta Soldiers
Bell/Drum Towers
Da Fu Chang Dumplings (Third Floor, not the dumpling restaurant on the first/second floor)
City Wall
Tang Dynasty Show
Project Current Status
Enhancing Similarity Measurement by Utilizing Tags in Item-Based Collaborative Filtering
Abstract
In Item-Based collaborative filtering, there are many steps employed to predict the user's interests on various items. Similarity measurement is the first step of the process and influences the accuracy and precision of the final recommendation. The existing similarity measurement methods rarely utilize user defined tags to assist in analyzing how homogenous items are in the eyes of users. This paper aims to enhance the similarity measurement by combining traditional item-based collaborative filtering with tags. The process will follow three steps to calculate the similarity measurement. The first step is to calculate the similarity measurement only using weighted tags. The second step is to calculate the similarity measurements by using item-based collaborative filtering techniques, such as, Pearson Correlation, Cosine similarity, and Spearman ranking. The final step is to combine the results from the previous two calculations and continue from there with traditional item-based collaborative filtering, for example, nearest N neighbor and prediction techniques. We will then compare the prediction results using only traditional similarity measurement and our redefined similarity measurement.