Syllabuses - PG

CS977 - Advanced Information Retrieval

TIMETABLETEACHING MATERIAL
Credits20
Level5
SemesterSemester 1
AvailabilitySemester 1
PrerequisitesNone
Learning Activities Breakdown20 hours lab

20 hours lecture

160 hours self-directed study

Items of Assessment3
Assessment50% final written exam

40% coursework

10% weekly quizzes

 

LecturerCatherine Chavula

Aims and Objectives

The aim of this course is to introduce students to the major concepts of Information Retrieval (IR), including the design, implementation, and evaluation of Information Retrieval systems.

Learning Outcomes

  1. Students will be able to discuss key concepts such as relevance, in the context of Information Retrieval
  2. Students will be able to apply the theories and technologies used to construct modern Information Retrieval systems.
  3. Students will be able to critically evaluate the assumptions behind the evaluation of Information Retrieval systems
  4. Students will be able to design, implement and evaluate information retrieval systems or techniques.

Syllabus

  •  Introduction to search and relevance
  • Text pre-processing and indexing
  • Document representation
  • Traditional Information Retrieval models
  • Query expansion and relevance feedback
  • Information retrieval evaluation
  • Search interfaces
  • Learning to rank
  • Neural ranking models
  • Link analysis and Web search
  • Recommender systems
  • Multimedia search
  • Emerging topics in Information Retrieval

Recommended Reading

This list is indicative only – the class lecturer may recommend alternative reading material. Please do not purchase any of the reading material listed below until you have confirmed with the class lecturer that it will be used for this class.

Essential:

Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schuetze, 2008. Introduction to Information Retrieval. Cambridge University Press, 2008 Available at https://nlp.stanford.edu/IR-book/information-retrieval-book.html 

Francesco Ricci, Lior Rokach, Bracha Shapira and Paul B. Kantor. (Eds.). (2022). Third Edition Recommender systems handbook. Springer US. 

Omar Alonso and Ricardo Baeza-Yates (Eds.). 2024. Information Retrieval: Advanced Topics and Techniques (1st. ed.). ACM Books, Vol. 60. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3674127 

Further Reading:

Bhaskar Mitra and Nick Craswell. 2018. An Introduction to Neural Information Retrieval . Foundations and Trends in Information Retrieval 13, 1 (Dec 2018), 1–126. https://doi.org/10.1561/1500000061

Francesco Ricci, Lior Rokach, Bracha Shapira and Paul B. Kantor. (Eds.). (2011). Recommender systems handbook. Springer US. https://doi.org/10.1007/978-0-387-85820-3

 

Last updated: 2025-05-27 13:46:16