Information Search and Retrieval

Lecturer: Francesco Ricci

Academic year 2014-2015 - 2nd Semester

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Start date:  Thursday, February 26th 10:30-12:30, Room E411
Lectures: Tuesday  10:30-12:30, Thursday 10:30-12:30
Labs: Tuesday 14:00-16:00
Hours of availability for students and tutoring: Tue: 16:00-18:00, by prior arrangement via e-mail.

Objectives: 

The first objective of this course is to present the scientific underpinnings of the field of Information Search and Retrieval. We will be concerned with basic information retrieval concepts and more advanced techniques for information filtering and decision support. 

The World Wide Web has become the primary source of information for leisure and work activities and its huge content would be wasted if that information could not be found, analyzed, and exploited so that each user can quickly find information that is both relevant and comprehensive for their needs. Moreover the Web has become a principal driver of innovation and a range of new techniques have been introduced to tame and exploit its information content. Personalization and information filtering techniques, e.g., Recommender systems, are now largely used, particularly in eCommerce web sites, for easing the information search and discovery processes, and increasing customer fidelity and conversion rates.

Hence, the second objective of this course is to provide to the student a rich and comprehensive catalogue of information search tools that can be exploited in the design and implementation of a specific Web site, such an eCommerce or eGovernment applications for travel and tourism or health.

Syllabus:

• Basic information retrieval concepts
• Boolean retrieval
• Indexing
• Vector space model
• Text and vector space classification
• Evaluation in information retrieval
• Recommender systems
• Collaborative- and Content-based filtering
• Hybrid recommender systems
• Context-dependent recommender systems
• Decision making
• Web search and link analysis
• Ranking and machine learning on documents

Exam

• The exam consists of two parts: project, final written exam. The student must pass all of them and each of them is evaluated with a grade: 9 <= P, W <= 15.
• The final grade is obtained as: F =  P + W. Laude is given to students with an exceptionally good project or exam.
• The project will consist of the design of an innovative system prototype for an information search and recommender system in a specific application scenario selected by the students. The project will be evaluated at the end of the semester.
• Below there are two examples of project reports that were prepared last years (with different requirements, so look carefully to the current requirements and project format).
  
• Examples: language-learning.pdf and sequential-web-pages-rec.pdf
• Example of written exam: Exam-2013-07-16.pdf
• Writtem exam date: July 8th, 2PM
• Presentation of projects: June 18th, 2:00PM
• Evaluation of the projects: evaluation-isr-projects-2015.pdf
• Written exam Semptember 22nd 2015: results
  

Reading Material

The suggested book for the information retrieval topics is:

• C. D. Manning, P. Raghavan and H. Schutze. Introduction to Information Retrieval, Cambridge University Press, 2008.

There is a new book dedicated to recommender systems that you may want to use:

• Ricci, F.; Rokach, L.; Shapira, B.; Kantor, P.B. (Eds.), Recommender Systems Handbook. 1st Edition., 2011, 845 p. 20 illus., Hardcover, ISBN: 978-0-387-85819-7 (a new edition is going to be published on 2015)
  

There is also a good collection of papers on personalized and adaptive and some of these papers will be suggested as reading material:

• Brusilovsky, Peter et.al. The Adaptive Web: Methods and Strategies of Web Personalization. Berlin: Springer, 2007. http://www.springerlink.com/content/x646782t122p/

All the required reading material will be provided during the course and will be available in electronic format. Copy of the slides will be available as well.

Lectures

Part 1   -  Introduction to Information Retrieval and Recommender Systems - part1.pdf

• Reading Material
• Andrei Z. Broder: A taxonomy of web search. SIGIR Forum 36(2): 3-10 (2002)  [.pdf ]
• Gary Marchionini: Exploratory search: from finding to understanding. Commun. ACM 49(4): 41-46 (2006)  [.pdf ]
• Resnick, P. and Varian, H. R. (1997). Recommender systems. Communications of the ACM,40(3):56-8. [.pdf ]
• Carol Collier Kuhlthau: Information Search Process
  
• Lab 1 - March 3rd
  
• Pagerank lecture notes: google.pdf

Part 2 - Boolean Retrieval - part2.pdf

• Reading Material
• IIR Chapter 1, Section 2.3: Faster postings list intersection via skip pointers, Section 3.1: Search structures for dictionaries
• Lab 2 - March 10
  
• Lab slides: lab-part1.pdf, PageRank.zip
  

Part 3 -  Dictionaries and tolerant retrieval - part3.pdf

• Reading Material
• IIR Chapter 2, Sections: 2.1, 2.2, 2.4. Chapter 3, Sections 3.2, 3.3.
• Lab 3 - March 17th
  
• Lab slides: lab-part2.pdf
  

Part 4 -  Index Construction - part4.pdf

• Reading Material: IIR Chapter 4.

Part 5 - Scoring, Term Weighting and the Vector Space Model - part5.pdf

• Reading Material: IIR Book, Sections 6.2, 6.3, 6.4 (excluded 6.4.4)
• Lab 4 - March 24th
  
• Lab slides: projects.pdf

Part 6 - Scoring in a Complete Search System - part6.pdf

• Reading Material: IIR Book, Sections 7.1, 7.2.

Part 7 - Evaluation of  Information Retrieval Systems - part7.pdf

• Reading Material: IIR Book, Chapter 8.
• Lab 5 - March 31st
  
• lab slides: lab-part3.pdf
  

Part 8 - Relevance feedback - part8.pdf

• Reading Material: IIR Book, Chapter 9 

Part 9 - Text classification and Naive Bayes - part9.pdf

• Reading Material: IIR Book, Chapter 13 - Tom Mitchell, Machine Learning.  McGraw-Hill, 1997.
• Lab 6 - April 10th
• Presentation of students' ideas for projects
  
• Lab slides: lab-part3.pdf

Part 10 - Vector space classification - part10.pdf

• Reading material: IIR Book, Chapter 14 - Tom Mitchell, Machine Learning.  McGraw-Hill, 1997.
• Lab 7 - April 14
• Lab slides: lab-part4.pdf lab-part5.pdf
  

Part 11: Collaborative Filtering - part11.pdf

• Reading material:
  
• J. Ben Schafer, Dan Frankowski, Jonathan L. Herlocker, Shilad Sen: Collaborative Filtering Recommender Systems. The Adaptive Web 2007: 291-324 [ .pdf ]
• Christian Desrosiers, George Karypis: A Comprehensive Survey of Neighborhood-based Recommendation Methods. Recommender Systems Handbook 2011: 107-144 [pdf]
• Lab 8  -  April 21
  
• Lab slides:lab-part5.pdf lab-part6.pdf
  

Part 12: Advanced Topics in Collaborative Filtering - part12.pdf

• Reading material: Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. In ACM Conference on Electronic Commerce, pages 158-167. [ .pdf ] 
  
• Lab 9 -  April 28
  
• Lab slides: lab-part6.pdf lab-part7.pdf

Part 13: Item-to-Item Filtering and Matrix Factorization - part13.pdf

• Reading material: 
• Sarwar, Karypis, G., Konstan, J., and Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of WWW10 Conference, pages 285-295, Hong Kong. ACM. [.pdf ]
• Koren, Y.; Bell, R.; Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems.  IEEE Computer, Volume 42, Issue 8, p.30-37 [.pdf]
• Lab 10 - May 5
  
• Lab slides: lab-part8.pdf lab-part9.pdf

Part 14: Content-Based Filtering and Hybrid Systems - part14.pdf

•  Reading material: 
• Robin Burke. Hybrid web recommender systems. In The Adaptive Web, page 377-408. Springer Berlin / Heidelberg, 2007. [ .pdf ]
• Michael J. Pazzani and Daniel Billsus. Content-based recommendation systems. In The Adaptive Web, page 325-341. Springer Berlin / Heidelberg, 2007. [.pdf ]
• Lab 11 -  May 12
  
• Lab slides: lab-part9.pdf , lab-part10.pdf, solution-ex-classification.xls
  

Part 15: Context-Dependent Information Filtering - part15.pdf

• Reading material:
• G. Adomavicius and A. Tuzhilin. Context-Aware Recommender Systems. In Recommender Systems Handbook, 217–256. Springer Verlag, 2011. [.pdf]
• Lab 12 -  May 19
  
• Lab slides: lab-part10.pdf, exercises-RSs.pdf, solutions-RSs-exercises.zip
  

Part 16: Recommendations for groups  - part16.pdf

• Reading material:
• Judith Masthoff. Group Recommender Systems: Combining Individual Models. In Recommender Systems Handbook, 677–702. Springer Verlag, 2011. [.pdf]

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