1. Course grading Project: 75% Broken into several incremental deliverables Paper appraisal/evaluation/project tool evaluation in earlier May: 25%

2. Paper appraisal Read and critically appraise the system/tool your project uses and/or a recent research paper (e.g. SIGIR, WWW conferences) which is relevant to your project By April 24, obtain instructor confirmation on content you will read. Propose what to do no later than April 17 By May 12 turn in a slide report/web site Summarize and include relevant content. Compare it to other work in the area Discuss interesting issue/research directions that arise. Get ready for a short presentation.

3. Project Opportunity to devote time to a substantial research project Typically a substantive programming project. Or in-depth analysis/comparison of methods and evaluation. Work in teams of 1 or 2 students Topic can be selected towards your interests Meet with me to discuss options

4. Project Due April 14: Project group and project idea Decision on project group Brief description of project area/topic We’ll provide initial feedback Due April 21: Project proposal Should break project execution into three phases – Block 1, Block 2 and Block 3 Each phase should have a tangible deliverable Block 1 delivery due May 5 Block 2 due May 19 Block 3 (final project report) due June 9th Week of June 7: Student project presentations

5. Project - breakdown 10% for initial project proposal Scope, timeline, cleanliness of measurements Writeup should state problem being solved, related prior work, approach you propose and what you will measure. 10% for deliveries each of Blocks 1, 2 30% for final delivery of Block 3 Must turn in a writeup Components measured will be overall scope, writeup, code quality, fit/finish. Writeup should be ~8 pages

6. Project Presentations Project presentations in class (about 10 mins per group): Great opportunities to get feedback. April 23/26: Students present project plans Week of June 7: Final project presentations

7. What is next? Project examples Example of Tools WordNet Google API Amazon Web Services / Alexa Lucene Stanford WebBase

8. Project examples Leveraging existing theory/data/software is encouraged, e.g.: Web services WordNet Algorithms and concepts from research papers Etc. Most projects: compare performance of several methods, or test a new idea against some baseline

9. Project Ideas Build a search engine for UCSB technical reports. Compare and improve the ranking algorithm. Crawl pages of a particular subject and build a special database and ranking (e.g. wikipedia) Classify pages based on wikipedia or DMOZ categories.

10. Lucene http://jakarta.apache.org/lucene/docs/index.html Easy-to-use, efficient Java library for building and querying your own text index Could use it to build your own search engine, experiment with different strategies for determining document relevance, …

11. Google API http://code.google.com/intl/en/apis/ajaxsearch Web service for querying Google from your software ~1M queries per day. Web search, site search, news search, blog search, Note: within search requests you can use special commands like link, related, intitle, etc.

12. WordNet http://wordnet.princeton.edu/ Java API available (already installed) Useful tool for semantic analysis Represents the English lexicon as a graph Each node is a “synset” – a set of words with similar meanings Nodes are connected by various relations such as hypernym/hyponym (X is a kind of Y), troponym, pertainym, etc. Could use for query reformulation, document classification, …

13. Stanford WebBase http://www-diglib.stanford.edu/~testbed/doc2/WebBase/ They offer various relatively small web crawls (the largest is about 100 million pages) offering cached pages and link structure data They provide code for accessing their data

14. Recommendation systems Data: http://www.grouplens.org/node/12 Rating of 270K books from 278K users. Rating of 100 jokes from 73K users.

15. “Natural language” search Present an interface that invites users to type in queries in natural language Find a means of parsing such questions of important categories into full-text queries for the engine. What is Why is How to Evaluate the relevancy of query answering.

16. Text spamming detection

17. Detecting index spamming lots of “invisible” text in the background color There is less of that now, as search engines check for it as sign of spam Questions: Can one use term weighting strategies to make IR system more resistant to spam? Can one detect and filter pages attempting index spamming? E.g. a language model run over pages [From the other direction, are there good ways to hide spam so it can’t be filtered??]