| Advanced Topics in Information Systems |  |
Academic year 2008-2009 - 1st Semester
Start date: Tue October 7, 2008
Lectures: Tue 10:30-12:30 - Room D003
Labs: Tue 14:00-15:00 - Computer Room E431
Hours of availability for students and tutoring: Tue: 15:00-16:30, by prior arrangement via e-mail.
Objectives: In this course we shall first discuss the motivations for the introduction of recommender systems in eCommerce web sites, i.e., for easing the customer information search and discovery process, and increasing fidelity and conversion rates. Then, the main techniques that have proposed in the last five years in the area of recommender systems will be presented. The objective is to provide to the student a rich and comprehensive catalogue of tools that can be exploited in the design and implementation of a personalized eCommerce application. Such a toolbox will enable the student to focus on an application domain (such as travel and tourism, rather than news or music or computers or digital cameras) and design an up-to-date information search and product recommendation component. Moreover this course aims at providing to the students a set of methodologies for evaluating the effectiveness of the proposed technical solution with off-line evaluations and by means of user studies.
Academic year 2007/2008 - Advanced Topics in Information Systems old web site
- Web site personalization
- Collaborative-based filtering
- Basic information retrieval concepts
- Content-based filtering
- Demographic-based and utility-based filtering
- Hybrid methods
- Knowledge based recommenders
- Conversational recommender systems
- Evaluation of recommender systems
- Human computer interaction and recommender systems
- Context dependent recommender systems
- Decision making
- 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 exceptionally good project or exam.
- The project will consist in the preparation of a feasibility study for a recommender system in a specific application domain selected by the students. The project outcomes are a written report (~ 5.000 words) and a presentation. The report must provide background information on recommender 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. More information on the projects (including the description of the work and possible types of recommender systems) are described here.
- The projects presented in last academic year can be found here.
- To be admitted to the written exam you must have already presented the project.
- Previous Exams: Winter Session 2006/2007; Winter Session 2007/2008; Summer Session 2007/2008; Fall Session 2007/2008.
- Winter session: written exam on February 16 (8:30-10:30). Projects presentation on February 2, (14:00-16:00) in Room D003. On February 2nd you must send to me your reports if you want to attend the written exam on February 16. If you want my feedbacks on the drafts please send them not after than January 20th.
- Summer session: written
exam on June 17 (16:00-18:00). For those that have not yet submitted
the project, please send the report to me before June 9.
- Projects' evaluation
- Evaluation of the written exam 18/2/2009 (text of the exam)
- Evaluation of the written exam 15/9/2009 (text of the exam)
There is no book dedicated to recommender systems. There is a good collection of papers on personalized and adaptive systems with some papers on recommender system. 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.This topic list could be updated during the course.
- Lecture 1 - Introduction to Recommender Systems - (lect1.pdf) October 7, 10:30 room D0.03
- Resnick, P. and Varian, H. R. (1997). Recommender systems. Communications of the ACM,40(3):56-58. [.pdf ]
- Anna Goy, Liliana Ardissono, and Giovanna Petrone. Personalization in e-commerce applications. In The Adaptive Web, pages 485-520. Springer Berlin / Heidelberg, 2007. [.pdf ]
- I suggest also looking at this movie by prof. Barry Schwartz: The Paradox of Choice - Why More is Less
- Lecture 2 - Collaborative-Based Filtering and Evaluation of Recommender Systems - (lect2.pdf - lab2.pdf) October 14, Lect 10:30 room D0.03 - Lab 14:00 room E431
- J. Ben Schafer, Dan Frankowski, Jonathan L. Herlocker, Shilad Sen: Collaborative Filtering Recommender Systems. The Adaptive Web 2007: 291-324 [ .pdf ]
- Lecture 3 - Advanced issues in Collaborative-Based Filtering - (lect3.pdf - lab3.pdf) October 21, Lect 10:30 room D0.03 - Lab 14:00 room E431
- 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 ]
- Lecture 4 - Item-to-Item Collaborative-Based Filtering - (lect4.pdf - lab4.pdf) October 28, Lect 10:30 room D0.03 - Lab 14:00 room E431
- Linden, G., Smith, B., and York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76-80. [.pdf ]
- 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 ]
- Lecture 5 - Content-Based Methods - (lect5.pdf - lab5.pdf) November 4, Lect 10:30 room D0.03 - Lab 14:00 room E431
- 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 ]
- Lecture 6 - Hybrid Methods - (lect6.pdf - lab6.pdf) November 11, Lect 10:30 room D0.03 - Lab 14:00 room E431
- Robin Burke. Hybrid web recommender systems. In The Adaptive Web, page 377-408. Springer Berlin / Heidelberg, 2007. [ .pdf ]
- Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13:393-408. [.pdf ]
- Lecture 7 - Knowledge Based Recommenders - (lect7.pdf ) November 18, Lect 10:30 room D0.03 - Lab 14:00 room E431
- Lab: Students must present their initial ideas about their projects, not more than 5 minutes for each group ( 2/3 slides)
- 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 ]
- Lecture 8 - Decision Making - (lect8.pdf ) November 25, Lect 10:30 room D0.03 - Lab 14:00 room E431
- Lab: it will be in room A101, for the seminar by P. Cremonesi, "A Large-Scale Recommender System for an IP Television Service Provider"
- lBarry Schwartz and Andrew Ward, Doing Better but Feeling Worse, In P. A. Linley, & S. Joseph (Eds.), Positive Psychology in Practice. Hoboken, N.J.: John Wiley and Sons, pp. 86-104. 2004. [ .pdf ]
- Lecture 9 - Conversational Systems - (lect9.pdf - lab9.pdf) December 2, Lect 10:30 room D0.03 - Lab 14:00 room E431
- N. Mirzadeh and F. Ricci. Cooperative query rewriting for decision making support and recommender systems. Applied Artificial Intelligence, 21(10): 895-932 (2007) [ .pdf]
- Lecture 10 - Comparison-Based and Critique-Based Recommender Systems - (lect10.pdf ) December 9, Lect 10:30 room D0.03 - Lab 14:00 room E431
- Lab: it will be in room A101, for the seminar by C. Palmisano, "A Data Mining Strategy For Targeted Sales Actions: A Case Study"
- 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 ]
- Lecture 11 - Context Dependent Recommender Systems (lect11.pdf ) December 16, Lect 10:30 room D0.03 - Lab 14:00 room E431
- 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 ] - Lecture 12 - Summary and research challenges (lect12.pdf ) January 13 2009, Lect 10:30 room D0.03 - Lab 14:00 room E431 (Student projects revision)
- Adomavicius, G. and Tuzhilin, A. (2005a). Personalization technologies: a process-oriented perspective. Commun. ACM, 48(10):83-90. [.pdf ]
- Adomavicius, G. and Tuzhilin, A. (2005b). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734-749. [.pdf ]