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title Course Project

The course project is to give the students hands-on experience on solving some novel information retrieval problems. The project appreciates either research-oriented problems or "deliverables." It is preferred that the outcome of your project could be publishable, e.g., your (unique) solution to some (interesting/important/new) problems, or tangible, e.g., some kind of prototype system that can be demonstrated. Bonus points will be given to the groups meet either one of above criteria. Group work (2-3 students) is strongly encouraged, but not required.

General steps

  • Pick a topic
  • Form a team
  • Survey related work
  • Work on the project
  • Write a report
  • Present the project

Your project will be graded based on the following required components:

  • Project presentation (50%)
    • 10 minutes presentation about what you have done for this course project. Format could be tailored according to the nature of the project, e.g., slides presentation and/or system demo.
    • Performance will be graded by both instructor and other students.
  • Project report (50%)
    • Detail written report of your project.
    • Quality requirement is the same as research papers, i.e., in formal written English and rigorous paper format.
    • Due on the last week of course before project presentation.

The official rubric for the final report and rubric for the project presentation is provided here.

Note that you are required to use the provided templates for your project proposal and final report. See the Resources page for the template and example file.

Pick a topic

You can either pick from a list of sample topics provided by the instructor or choose your own topic. Your starting point could be the essays that you wrote for Homework 1.

Leveraging existing resources is especially encouraged as it allows you to minimize the amount of work that you have to do and focus on developing truly your ideas.

When picking a topic, try to ask yourself the following questions:

  • What is exactly the (research) problem that you want to solve? Will it matter if nobody realizes this problem?
  • What kind of changes could your project make to the others?
  • Is there any existing alternative? If so, why do you still want to do it? How is your idea different from theirs? Would people appreciate about the difference?
  • What would be the major challenge(s) in this problem? Any specific background or resource you have to solve the identified problem?
  • What is the minimum goal to be achieved during this semester? (Try to drop everything non-essential and only keep the part that is truly novel.)
  • How do you plan to demonstrate that method to be developed is indeed solving the pain? Empirical experimentation and/or demo are required, unless you are doing a purely theoretic work.

Form a team

You are encouraged to work with other students as a team. Teams may consist of up to three total students. Teamwork not only gives you some experience on working with others, but also allows you to work on a larger (presumably more important) topic.

Note that it is your responsibility to figure out how to contribute to your group project, so you will need to act proactively and in a timely manner if your group leader has not assigned a task to you. The instructor will believe all team members actively contribute to the project and the same grade will be applied to the group member (unless special treatment is required).

Survey related work

While choosing a topic, it is very important to be aware of whether the problem you would like to tackle has already been solved. If so, you may want to figure out where exactly your novelty is and whether novelty leads to any benefit to others. Your goal is to go beyond, rather than simply duplicate, the existing work. To minimize your effort, you are encouraged to leverage existing algorithms, toolkits, and other useful resources as much as possible. The instructor can also help you check related work. Please feel free to discuss your plan with the instructor before finalizing your proposal.

Work on the project

You should leverage any existing tools or methods as much as possible. For example, consider using the Lucene toolkit for indexing and searching in a large text corpus; using Stanford NLP parser or OpenNLP toolkit for text analysis; using MALLET or WEKA for classification or clustering. There are also many tools available on the Internet. See the resources page for some useful pointers. Discuss any problems or issues with your teammates or classmates. If you need special support, please let the instructor know.

Consider documenting your work regularly. This way, you will already have a lot of things written down by the end of the semester. In addition, we strongly suggest using version control for your project! Nothing is more frustrating than losing a lot of your hard work, especially if it's close to a deadline.

Present the course project

At the end of the semester, each project team is expected to present their project in class. The purpose of this presentation is

  • Let you know about others' projects.
  • Give you some opportunity to practice presentation skills, which are very important for a successful career in both academia and industry.
  • Obtain some feedback from others about your project.

In general, the structure of your presentation should be prepared like a conference presentation. So it should touch all the following aspects (text in parenthesis states the instructor's expectation):

  • What is the background/motivation of your work? What research question will you address? (Learn how to attract public attention.)
  • Why is this problem important? (Learn to how persuade others.)
  • Is there any existing work? How novel is yours? (Learn how to sell your ideas.)
  • How did you solve this problem? (Learn how to deliver your solution.)
  • How good was your method? (Learn how to quantitatively/qualitatively evaluate your work.)
  • Any ideas for further improvement? (Learn how to look ahead.)

Think about how you can best present your work so as to make it as easy as possible for your audience to understand your main messages. Try to be concise, to the point. Pictures, illustrations, and examples are generally more effective than text for explaining your project. Try to show screen shots and/or plots of your experimental results. Watching some top conference presentations (e.g., KDD, SIGIR, ICML) on VideoLectures will be beneficial.

In order to be fair to all members in the same group, the instructor will randomly pick team members for question answering during the presentation.

Write a project report

You should write your report as if you were writing a regular conference paper. You should address the same questions as those you have addressed in the proposal and presentation, only with more details. Pay special attention to the challenges that you have solved and your detailed solutions. Basic sections to be included in the report should be the same as those in a conference paper, e.g., abstract, introduction, related work, method, experiment and conclusion. If you are developing a demo system or toolkit, your report should follow the format of a demo paper.

You are required to use LaTeX for your project report. See the Resources page for the template and example file. The project report must be at most ten pages with that format (no minimal requirement, as long as you feel it is sufficient to prove the merit of your work).

The instructor will provide feedback about your course project during the final presentation if we see any way to further improve your work, and bonus points will be given immediately.

Sample topics for course project

The instructor has selected a set of representative course projects from former UIUC students. From this selection, you can get a broad spectrum of topics and a basic sense of what would be regarded as feasible topics for our course project. Note, their ideas nor solutions are not necessarily perfect. You are encouraged to come up with better ideas and solutions.

Warning, please do not disclose or distribute the following project reports outside this class; their original authors hold all the reserved copyrights!

  • Specialized search engines

    • "Codify: Code Search Engine", Codify is a vertical search engine focusing on searching code and coding problems due to it’s ability to search with special characters.
    • "SaleTags", SaleTags is a web application that allows men to perform a natural language search for men’s clothing on sale at various retail stores.
    • "ReviewsHub", an integrated search and comparison system that allows users to focus on a specific feature of a product.
    • "Courducate", an MOOC Search and Recommendation System.
    • "Hotel Review Search", to provide a text-based search interface for retrieval and recommendation of hotels based on their reviews.
    • "WOOF", a user defined news recommendation system.
    • "ENTREEREC", an automatic entree recommending technique based on Yelp reviews.

  • Text mining

    • "SideEffectPTM", an Unsupervised Topic Model to Mine Adverse Drug Reactions from Health Forums.
    • "Better Hotel Reviews Report", improve hotel ratings by giving users the ability to remove unwanted reviews from consideration.
    • "Buy || !Buy: To Buy Or Not?", help consumers make intelligent buying decisions based on customer reviews posted on e-commerce websites.
    • "Understand Function of Location Entity", detect location related entities within a city from microblogs.
    • "KickUpper", a tool for making better crowdfunding projects.
    • "Article Topic Sentiment", perform like/dislike for an individual topic in an article.
    • "AskSnoo", verify social content against an established information source.
    • "Re:Search", identify professors to work with.
    • "Check the news", verify the accuracy and validity of different news sources regarding financial status of companies.

  • Visualization

  • Social Network

    • "Find-A-Friend", finds potential best friend by analyzing the online profile programmatically.
    • "BuddyMeet", a platform for students in U of I to explore and initiate events/activities.
    • "Destination Finder", destination recommendation based on social suggestions.

  • Developments

    • "Review Union", ReviewUnion mobile application which fetches the the product information from Walmart and Amazon to help customers compare product information from different website and also have a quick reference to review and feedback of that product.

Topics proposed by the instructor

  • Making sense of #hashtag in Twitter: Trends in twitter are transient and hard to understand due to the informality of the language. It will be helpful to explain/link those hashtags to external references (e.g., wiki, news article) for general audience to understand the trend and topic in Twitter.

  • Automatic tutor for English writing: Good English writing skill is always a significant challenge for a non-English speaker. And it is also even stressful for a native speaker in specific scenarios, e.g., formal scientific writing. This project aims to develop automatic tools based on language models to beautify an amateur's English writings. For example, language models trained on twitter data would make an ordinary user's tweet looks more like being written by a experienced twitter user; and language models trained on scientific publications would make an amateur's paper read like an expert's work.

  • Spatial temporal analysis of opinions: Social opinions provide a gold mine for researchers to understand the explore public's opinion towards a specific entity, e.g., products and celebrities, or a service, e.g., hotels and restaurants. This project aims to extend an existing aspect-based opinion mining system, ReviewMiner (http://timan100.cs.uiuc.edu:8080/ReviewMiner), for supporting spatial-temporal analysis of opinions. Specifically, we want to visualize the opinions: display the temporal dynamics of opinion across different entities (e.g., from twitter stream or reviews), render the opinions on a map, and support user interaction with such spatial-temporal analysis of opinions.

  • Social influence v.s. homophily: Users in Yelp write reviews about businesses and make friends who share similar tastes and preferences. However it is unknown whether users become friends because they visited the same restaurants before (i.e., homophily); or they visited the same restaurants because they were friend (i.e., )influence. Distinguishing these two factors are very important for social network based recommendations.

  • Query intent classification: Current product search system can only support simply keyword search, e.g., "canon 5d3". It is preferred if the system can support some simple semantic search, e.g., "cheap digit camera with high resolution." The system should be able to correctly map the specifiers of "cheap" and "high" to corresponding aspects of the product, e.g., price and image quality, and return all the results matching such criteria. One can imagine this as a translation process and opinionated review text documents provide nice resource to estimate such translation model.

Rubric for project report

Aspects Score
Strictly follow the provided template [0-10]
Background and research question were clearly stated in the introduction and the logic and argument were reasonable [0-10]
Contribution of the work was properly articulated in the introduction [0-10]
Sufficient discussion of state-of-the-art in related work section [0-10]
Description of the proposed method was clear, comprehensive, coherent and consistent with the claim in the introduction [0-20]
Precise description of experiment design and experimental data set [0-10]
Thorough experimentations that proved all necessary components in the proposed method and detailed analysis of the experimental results [0-20]
Summarization of the work, reasonable discussion of limitation of the proposed solution and future work [0-10]

Rubric for project presentation

Aspects Score
Slides content was clearly visible and self-explainable [0-10]
Presenters were confident about their work and clearly explained it to me [0-10]
Background and research question were clearly highlighted, and the logic and argument were reasonable [0-10]
There was sufficient discussion of state-of-the-art and why do we need this new method [0-10]
Description of the proposed method is clear, comprehensive, coherent and consistent with the claim in the introduction [0-15]
Thorough experimentations that proved all necessary components in the proposed method and detailed analysis of the experimental results [0-15]
The presenters well managed their time during presentation. [0-10]
The presenters did a good job in answering the questions. [0-10]
I like this work! [0-10]

Final presentation schedule

We will stick to the following order in our final project presentation. Each team has 10 mintues for presentation and 2 minutes for question answering.

No. Title Group
1. Image classification using deep learning techniques Zachary Verham, Soheil Nematihaji
2. Math search engines Andrew Norton, Benjamin Haines
3. Word Mover's Distance (WMD) to search similar commit comments Chengjun Yuan
4. Music Semantic Search Nicholas Milef, Nathan Thillairajah
5. Tilde search Joseph Barrow, Hunter Leath, Michael Snider
6. Privacy preserving information retrieval Wasi Ahmad, Md Masudur Rahman
7. Conversation model to understand and answer technical questions Debajyoti Datta
8. Context-aware Business Recommendation System Lihua Cai
9. Recommend potential friends through yelp review Haoran Liu
10. Exploitation-Exploration Balance in Collaborative Filtering based Recommender System Huazheng Wang,Ji Gao,Jian Kang
11. Latent aspect rating analysis on yelp review Yue Hu, Yuting Wang, Yin Zhang
12. Topic model in news analysis Meiyi Ma, Chenxi Guo, Renqin Cai
13. Trusted rating propagation Paul Vasiu