With this function you can construct your weekly calendar of lessons, which is customized on the basis of the courses that you intend to follow. Warning: the personal schedule does not replace the presentation of the study plan! It's an informal tool that can help you better manage the organization of class attendance before the study plan presentation. After the study plan presentation we recommend you to use the Lecture timetable service in your Online Services.
To create your customized schedule follow these instructions:
Click on the "Enable" link to proceed. You will be asked your surname and first name in order to determine your alphabetic grouping.
To add or remove courses from your personal schedule, use the small icons which are found next to the courses:
addition of the course
removal of the course
selection of the section of the Laboratory of Architecture (Note: the effective area in which the teaching will be carried out will be determined after the presentation of the Study Plans)
The sidebar on the left displays the number of lessons included in schedule. There are also these commands:
View the schedule: allows the viewing of the weekly synoptic schedule
Delete the schedule: cancels the selections made
When you have finished the entry, you can print the calendar you have made.
Course completely offered in italian
Course completely offered in english
School of Industrial and Information Engineering
(Master of Science degree)(ord. 270) - MI (481) Computer Science and Engineering
T2A - COMPUTER SCIENCE AND ENGINEERING
Credits (CFU / ECTS)
Recommender systems aim to support users in their decision-making while interacting with large information spaces. They recommend items of interest to users based on preferences they have expressed, either explicitly or implicitly. Recommender systems help overcome the information overload problem by exposing users to the most interesting items, and by offering novelty, surprise, and relevance. This course gave a tutorial about the leading approaches in recommender systems. The techniques described touch both collaborative and content-based approaches and include a number of algorithms that result in techniques. The course also explore alternative techniques of evaluating recommender systems.