Information
Search and Retrieval |

|
Lecturer: Francesco
Ricci
Academic
year 2014-2015 - 2nd Semester
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:
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.
- Writtem exam date: July 8th, 2PM
- Presentation of projects: June 18th, 2:00PM
Reading
Material
The suggested book for the information retrieval topics is:
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:
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
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
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
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
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
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
Part
10 - Vector space classification - part10.pdf
- Reading
material: IIR Book, Chapter 14 - Tom Mitchell,
Machine Learning. McGraw-Hill, 1997.
- Lab 7 - April 14
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
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
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
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
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
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]