Course Overview

Various aspects of recommender systems such as methods, implementation, evaluation and problems will be introduced. More specifically, representative recommendation methods such as user-based collaborative filtering, item-based collaborative filtering (association rules), content-based filtering (which represents contents of items as various numericals), knowledge-based recommendation (which requires users to show their interests) and hybrid recommendation based on machine learning using various information will be introduced. How to measure appropriateness of recommendation i.e. notion of precision, recall, novelty or serendipity for users will also be shown.

Learning Achievement

(1) Overview of recommender system: Methods, representative implementations and problems (Tsuji)
(2) Collaborative filtering: User-based and item-based collaborative filtering (association rule) (Tsuji)
(3) Content-based filtering: Content representation, similarity measure and use of machine learning (Tsuji)
(4) Knowledge-based recommender system: Constraint-based and case-based recommendation (Tsuji)
(5) Hybrid recommender system: Switching and combining methods of recommendation based on users' situations (Matsumura)
(6) Evaluation of recommender system: Metrics such as precision, recall, novelty, diversity and serendipity, and experimental research design (Matsumura)
(7) Various state-of-the-art recommender systems: Systems which aim to improve new evaluation metrics and which take into consideration users' situations (contexts) such as their intention and position information (Matsumura)
(8) Developing recommender system (1): Basics of programming and implementation of collaborative filtering (Matsumura)
(9) Developing recommender system (2): Implementation of user/item-based collaborative filtering (Matsumura)
(10) Developing recommender system (3): Introduction to R and its recommendation packages (Tsuji)

Competence

Mathematical research ability, System expertise

Course prerequisites

Grading Philosophy

Assignments will be given based on course objectives and graded by the following policy:
Short test assigned in every class in manaba 60%, term paper 40%.

Course schedule

Course type

Lectures

Online Course Requirement

Instructor

Tsuji Keita,Matsumura Atsushi

Other information

Classes are offered in Japanese in odd-years and in English in even-years, respectively; Special subjects for Information Interaction; Teacher Training Course
Identical to 01MBB03.
Online(Asynchronous) Students are asked to view the course materials when convenient (i.e. on-demand type classes). The course materials can be obtained as PowerPoint files in manaba and can be viewed as movie files in Microsoft Stream.

Site for Inquiry

Link to the syllabus provided by the university