| Information Search and Retrieval |  |
Academic year 2009-2010 - 2nd Semester
Start
date: Tuesday,
February
23rd
8:30-10:30, Room E411
Lectures: Tuesday
8:30-10:30,
Thursday 11:00-13:00, Room E411
Labs:
Friday 16:00-18:00 - Room E431
Hours of availability for
students and tutoring: Tue:
15:00-16:30, by prior arrangement via e-mail.
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.
- 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
- Knowledge based recommenders
- Conversational recommender systems
- Evaluation of recommender systems
- Human Computer Interaction and recommender systems
- Context-dependent recommender systems
- Decision making
- Web search and link analysis
- Ranking and machine learning on documents
- 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 in the preparation of a system prototype for an information search and recommender system in a specific application domain selected by the students. The project results are a written report (~ 5.000 words), a system prototype and a presentation. The report must provide background information on the systems and describe the proposed one: description of the application problem, survey of existing applications and studies, evaluation of the pros and cons of alternative techniques, system functions and core techniques, advantages for the customer. The project will be evaluated at the end of the semester.
- Below there are two examples of project reports that were prepared last year for the "Advanced Topics in Information System" course - ATIS. Please note that ATIS was only on recommender systems, so these projects focussed only on that topic. You should consider in your projects the IR techniques as well.
- Examples: torrent-recommendations.pdf and climbing-route-recommendations.pdf
- Example of a possible written exam: ISR-Exam-2010-Example.pdf
- To be admitted to the written exam you must have already presented the project.
- Lab for the preparation of the written exam: June 23rd 2010 - Room E431 - 10:30 - 12:30
- Presentation of projects - Fall Session: September 8th 2010 - My office - 14:00 - 15:00 -
- Note that the project report must also be submitted by email to me on September 8th 2010.
- NEW: Projects' evaluations
- Exam Results: Summer Session 2010
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.
- E. Hatcher and O. Gospodnetić. Lucene in Action, 2nd Ed. Manning, 2010.
- Brusilovsky, Peter et.al. The Adaptive Web: Methods and Strategies of Web Personalization. Berlin: Springer, 2007. http://www.springerlink.com/content/x646782t122p/
Part 1 - Introduction to Information Retrieval and Recommender Systems - 01.pdf
- Reading Material
- Reading Material
- IIR Chapter
1, Section 2.3: Faster postings list
intersection via skip pointers, Section 3.1: Search structures
for
dictionaries
- Lab 1: Exercises-01-02.pdf
- Homework: Execises 1.7 and 1.8.
- Reading Material
- IIR Chapter 2, Sections: 2.1, 2.2, 2.4. Chapter 3, Sections 3.2, 3.3.
- Advanced search functionalities in google: http://www.google.com/support/websearch/bin/answer.py?answer=136861
- Reading Material: IIR Chapter 4.
- Lab 2: Exercises-03.pdf
- Reading Material: IIR Book, Sections 6.2, 6.3, 6.4 (excluded 6.4.4)
- Reading Material: IIR Book, Sections 7.1, 7.2.
- Lab 3: tutorial-lucene.pdf
- Reading Material: IIR Book, Chapter 8.
- Lab 4: Exercises-04.pdf
- Reading Material: IIR Book, Chapter 9
- Reading Material: IIR Book, Chapter 13 - Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
- Lab 5: tutorial-lucene-2.pdf
- Reading material: IIR Book, Chapter 14 - Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
- Lab 6: Exercises-05-06.pdf data-6.10-6.15-6.17.xls
- Reading material: J. Ben Schafer, Dan Frankowski, Jonathan L. Herlocker, Shilad Sen: Collaborative Filtering Recommender Systems. The Adaptive Web 2007: 291-324 [ .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 7: Exercises-07.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 8: Exercises-08-09.pdf solution chi-square exercise Exercise-chi-square.xls
- 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 9 April 30th 2010: Presentation of the your first idea on the exam projects: prepare 4/5 slides describing the domain, the system functions and first hypothesis on the technologies that you plan to use. Every group havs 10 minutes max for the presentation.
- Reading material:
- Barry Smyth. Case-based recommendation. In The Adaptive Web, pages 342-376. Springer Berlin / Heidelberg, 2007. [.pdf ]
- Ricci, F., Cavada, D., Mirzadeh, N., and Venturini, A. (2006). Case-based travel recommendations. In Fesenmaier, D. R., Woeber, K., and Werthner, H., editors, Destination Recommendation Systems: Behavioural Foundations and Applications. CABI. [ .pdf ]
- Lab 10: Exercises-10.pdf
- Reading material:
- G. Adomavicius, R.
Sankaranarayanan, S. Sen, and A.
Tuzhilin. Incorporating contextual information in recommender systems
using a multidimensional approach. ACM
Trans. Inf. Syst.,
23(1):103-145, 2005.
[ .pdf ] - Lab 11: Exercises-Rec.pdf
- Reading material:
- Reading material:
- McGinty, L. and Smyth, B. (2002). Comparison-based recommendation. In Craw, S. and Preece, A., editors, Advances in Case-Based Reasoning, Proceedings of the 6th European Conference on Case Based Reasoning, ECCBR 2002, pages 575-589, Aberdeen, Scotland. Springer Verlag. [.pdf ]
- Reilly, J., McCarthy, K., McGinty, L., and Smyth, B. (2005). Incremental critiquing. Knowledge-Based Systems, 18(4-5):143-151. [ .pdf ]