代寫 COMP3771 推薦系統 代寫python System Prototype
Assessment Brief
Module title User adaptive intelligent systems
Module code COMP3771
Assignment title Recommender System Prototype
Assignment type and
description
Coursework
Rationale The coursework will allow you to apply topics from the module in a practical
context. The coursework will allow you to practice key employability skills:
problem solving, creativity, product design, technical report writing.
Word limit and guidance maximum of 1500 words and no more than 6 pages (excluding references and
acknowledgements)
Use of GenAI in this
assessment
AMBER: AI tools can be used in an assistive role You are permitted to use AI
tools for specific defined processes within the assessment. See below for
further details.
Weighting 40% of the module grade
Submission deadline 10am, 21 November 2025
Submission method Submit a pdf file via Gradescope
Feedback provision Feedback will be provided via Gradescope no later than 3 weeks after the
submission deadline. Group feedback will be provided in a lecture, together
with the module wrapup at the end.
Learning outcomes
assessed
By completing the coursework, you will be able to:
• derive requirements for a user-adaptive system in a specific context;
• design and prototype a user-adaptive system using recommender
techniques;
• broaden your knowledge of recommender systems by researching
relevant literature;
• assess your proposed design for transparency and usability;
• assess the strengths and weaknesses of your proposed design;
• practice technical report writing.
Module leader Prof. Vania Dimitrova
Other Staff contact Dr. Mohammed Azhar Iqbal
Use of AI to create this
assignment
The initial drafting of section “1 Assignment guidance” in this assignment was
generated with the support of Copilot (Microsoft,
https://m365.cloud.microsoft/chat). All AI-generated content has undergone
thorough review and refinement to ensure accuracy, clarity, and relevance.
1. Assignment guidance
Your task is to design a prototype of a personal wellbeing assistant—a digital “buddy” that supports
individuals in leading a healthier and happier lifestyle. This assistant should offer personalised
recommendations tailored to the user’s unique needs and preferences.
Wellbeing aspect
The wellbeing assistant should act as a supportive companion, helping users make informed decisions about
their daily habits. It should provide tailored information and suggestions across one specific wellbeing
aspect. Possible wellbeing aspects can be:
• physical health (e.g., stay hydrated, be active, or improve sleep quality);
• mental wellbeing (e.g., mindfulness, mood awareness, or motivation);
• nutrition (e.g., meal planning, healthy cooking, or shopping);
• social connections (e.g., reaching out to friends, joining local events, or engaging in community
activities);
• daily routines (e.g., morning/evening rituals, time management, or relaxation).
Target user group
Your prototype should be designed for a specific user group. Clearly identify and describe the characteristics
of this group in your report. This will help demonstrate how your wellbeing assistant can offer personalised
support that is relevant and meaningful to the users’ lifestyle and challenges.
Possible target user groups include:
• students - you may choose to focus on students in a particular year, subject area, location, etc.
• people with special needs – you may consider individuals with physical disabilities, neurodivergent
individuals, patients recovering from illness, carers, etc.
• specific demographics - you may consider children, elderly people, newcomers to a foreign country,
etc.
Your report should include a clear rationale for selecting the target user group. Justify why this group has
unique wellbeing needs or preferences and describe what they are. Specify what kinds of personalisation
would be necessary to address these needs or preferences. Support your statements with appropriate
references.
2. Use of GenAI
You are required to follow the University’s guidelines regarding the use of Artificial Intelligence tools.
AI may be used in a supportive role to assist your academic work. Acceptable uses include:
• Conducting literature searches;
• Enhancing language and grammar;
• Generating new ideas.
To ensure privacy and data security, please use only the University’s secure Copilot access when engaging
with AI tools.
You must clearly acknowledge any areas where AI has contributed to your work. This includes referencing
any suggestions or content derived from AI tools. Be mindful of potential biases and the use of proprietary
content. For examples of appropriate acknowledgements and referencing, please refer to the guidance
provided here.
3. Assessment tasks
Write a report presenting your design. The report should be a maximum of 1500 words and no more than 6
pages, excluding references and the title page that should include the report title, student name, and
student ID.
Your report should be written in an academic style, and should include the following sections:
A. Introduction which includes:
• Justification of the selected target user group - describe the target group and justify with appropriate
references why this group of people is selected for your application and why personalisation is needed).
• Outline of the requirements for your application - list the requirements and describe how you came up
with them (e.g. this can be based on references you have found to justify user needs or personal
experiences, or feedback from potential users, e.g. interviews/short survey). By focusing on the
personalisation features, list both functional requirements (maximum 5) and non-functional
requirements (maximum 5).
B. Description of the recommender method that will be used, including:
• Justification of the selection of the recommender method using appropriate references to user adaptive systems that use this recommender method (note that it is not necessary that the user adaptive systems which have used the selected recommender method refer to wellbeing assistant.
• Description of the background data which will be used by the recommender method with appropriate
illustrations (include description of the background data, how the data will be collected or provided,
and how it will be represented).
• Description of the input data which will be used by the recommender method with appropriate
illustrations (include description of the input data, how the data will be collected or provided, and how
it will be represented).
• Description of the recommender algorithm including a brief outline of the main steps in the algorithm.
C. Critical review of the proposed design, which should include:
• Strengths - describe two strengths of the proposed design and provide appropriate justification and
illustration for each strength.
• Transparency - apply the checklist presented in the paper “Best Practices for Transparency in Machine
Generated Personalization” by Schelenz, Segal, and Gal [paper link available in Minerva] – apply all five
areas from Table 2 in the paper.
• Usability – familiarise with the usability challenges presented in the paper “Adaptive interfaces and
agents” by Jameson. [pages 15-18, paper link available in Minerva] and identify two possible usability
threats that are related to your proposed design, for each of these threats describe what appropriate
preventive and remedial measures should be included.
D. Video (3 minutes long) demonstrating your prototype, including:
• User scenario - the demo should be based on a user scenario that illustrates the user interaction with
the systems.
• Data collection – the demo should show what data is collected from the user and how.
• User interface – the demo should show what information is presented to the user and should illustrate
how the user will receive the recommendations.
• Online link to the video with a description how to access it.
Note that the demo should mock the data collection and the user interface. You are free to use any software
to develop your prototype. Low fidelity, e.g. storyboarding with PowerPoint or using wireframe software will
suffice for the task. If you prefer to use any high-fidelity prototyping, this will be fine too.
E. References and acknowledgements, including:
• acknowledging and proper referencing the use of AI, please refer to the guidance provided here
• referencing all sources cited in your report following Leeds Harvard referencing style
• noting all tools used to produce the report and the video prototype.
4. General guidance and study support
Resources
• Re-read articles issued in the module as you may find helpful ideas from these experts on the overall
architecture, the design of the user model, and the user modelling methods to be used.
• Visit User Modeling Inc (https://www.um.org/) and Recommender Systems conferences
(https://recsys.acm.org/) for examples of user-adaptive systems presented at past conferences.
Reference the systems that inspire the personalisation features to include in your application. Your
inspiration may come from another domain and can be adapted for the problem in this coursework.
• You should include references that come from scholarly outputs in adaptation and personalisation;
for instance, the RecSys (Recommender Systems) or UMAP (User Modelling, Adaptation and
Personalisation) conference series, the UMUAI (User Modeling and User-adapted Interaction) journal.
Support
• The coursework brief will be presented in a lecture
• Use the module Teams space to ask questions about the coursework (and the module in general)
• Prof. Dimitrova’s office hour is Friday 16pm-17pm, room 2.29, Bragg building.
5. Assessment criteria and marking process
The report will be marked using the criteria below. The video of the prototype will be accessed via the link
and instructions provided in the report.
Outline of the marking scheme:
Introduction 10 marks
Description of the recommender method 16 marks
Critical review 12 marks
Video presenting the prototype 28 marks
Write up 4 marks
Total 70 marks
6. Presentation and referencing
This coursework will be submitted as a written report.
The quality of written English will be assessed in this work. As a minimum, you must ensure:
• Paragraphs are used
• There are links between and within paragraphs although these may be ineffective at times
• There are (at least) attempts at referencing
• Word choice and grammar do not seriously undermine the meaning and comprehensibility of the
argument
• Word choice and grammar are generally appropriate to an academic text
These are pass/ fail criteria. So irrespective of marks awarded elsewhere, if you do not meet these criteria,
you will fail overall.
7. Submission requirements
Submit a pdf file with your report in Gradescope. You can access Gradescope directly or via the assignment
link in module’s Minerva space.
8. Academic misconduct and plagiarism
Leeds students are part of an academic community that shares ideas and develops new ones.
You need to learn how to work with others, how to interpret and present other people's ideas, and how to
produce your own independent academic work. It is essential that you can distinguish between other
people's work and your own, and correctly acknowledge other people's work.
All students new to the University are expected to complete an online Academic Integrity tutorial and test,
and all Leeds students should ensure that they are aware of the principles of Academic integrity.
When you submit work for assessment it is expected that it will meet the University’s academic integrity
standards.
If you do not understand what these standards are, or how they apply to your work, then please ask the
module teaching staff for further guidance.
By submitting this assignment, you are confirming that the work is a true expression of your own work and
ideas and that you have given credit to others where their work has contributed to yours.
9. Assessment/ marking criteria grid
Detailed marking scheme
Section Feedback Marks
available
Introduction - Selected target user group properly justified (2 marks);
- Justification uses appropriate references (2 marks)
- Appropriate method to derive requirements is used (3 marks)
- Description of requirements (3 marks)
10
Description
of the
recommende
r method
- The selection of the recommender method properly justified (2 marks)
- The justification uses appropriate references to user-adaptive systems
that use this recommender method (2 marks)
- Background data properly described (4 marks)
- Input data properly described (4 marks)
- Appropriate description how background and input data will be used to
produce recommendations (4 marks)
16
Critical
review
- Strength 1 properly described and illustrated (2 marks)
- Strength 2 properly described and illustrated (2 marks)
- Transparency check list properly applied (4 marks)
- Usability threat 1 properly described and appropriate preventive measure
suggested (2 marks)
- Usability threat 1 properly described and appropriate preventive measure
suggested (2 marks)
12
Video
presenting
the
prototype
- User scenario appropriate (4 marks)
- The prototype demo shows clearly what data is collected about the user
(4 marks)
- The prototype demo shows clearly what information is shown to the user
(4 marks)
- The prototype demo shows clearly how the system adapts to the user (6
marks)
- The prototype meets the requirements specified in the introduction (6
marks)
- The demo properly links to the user scenario (4 marks)
28
Write up - Appropriate report structure (1 mark)
- Appropriate formatting (1 mark)
- Appropriate use of illustrations (1 mark)
- Appropriate referencing (1 mark)
4
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