Developing Explainable Recommendation Systems

Information on this PhD research area can be found further down this page under the details about the Widening Participation Scholarship given immediately below.

Applications for this PhD research are welcomed from anyone worldwide but there is an opportunity for UK candidates (or eligible for UK fees) to apply for a widening participation scholarship.

Widening Participation Scholarship: Any UK candidates (or eligible for UK fees) is invited to apply. Our scholarships seek to increase participation from groups currently under-represented within research. A priority will be given to students that meet the widening participation criteria and to graduates of the University of Salford. For more information about widening participation, follow this link: https://www.salford.ac.uk/postgraduate-research/fees. [Scroll down the page until you reach the heading “PhD widening participation scholarships”.] Please note: we accept applications all year but the deadline for applying for the widening participation scholarships in 2024 is 28th March 2024. All candidates who wish to apply for the MPhil or PhD widening participation scholarship will first need to apply for and be accepted onto a research degree programme. As long as you have submitted your completed application for September/October 2024 intake by 28 February 2024 and you qualify for UK fees, you will be sent a very short scholarship application. This form must be returned by 28 March 2024. Applications received after this date must either wait until the next round or opt for the self-funded PhD route.

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Project description: We are seeking highly motivated individuals to join our research group as Ph.D. students in the field of Explainable Recommendation Systems. The successful candidate will work on cutting-edge research projects that aim to make recommendation systems more transparent, interpretable, and accountable to users.

Background: Recommendation systems are widely used in various applications such as e-commerce, social media, and content platforms. However, most of these systems operate as black boxes, leaving users with little to no understanding of how recommendations are generated and making it difficult to build trust in the system. In recent years, there has been a growing interest in developing "explainable recommendation systems" that provide transparent and understandable explanations of recommendations. They not only increase the persuasiveness of the recommendations, but also, they can help users to make informed decisions. However, developing effective explanations is a challenging task that requires a deep understanding of both the underlying algorithms and the needs of the users.

The project will focus on the following research questions:

•   How can we design recommendation systems that are not only accurate but also provide users with explanations for recommendations?

•   What are the different methods for generating explanations in recommendation systems?

•   How can we evaluate the effectiveness of explainable recommendation systems?

Specific responsibilities include:

·   Conducting a comprehensive literature review of existing methods for Explainable Recommendation Systems.

•   Developing novel techniques for building Explainable Recommendation Systems

•   Evaluating the effectiveness of the developed methods

•   Publishing research findings in top-tier conferences and journals in the field.

•   Collaborating with other researchers in the team.

Requirements:

•   A Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

•   Strong programming skills in programming languages (e.g., Python, R, …).

•   Background in machine learning and data mining techniques. Candidates with experience in recommender systems and/or Explainable AI are particularly encouraged to apply.

•   Excellent analytical and problem-solving skills.

•   Good written and verbal communication skills

Please send your detailed CV to Dr. Azadeh Mohammadi [Email Address Removed]

Set the email subject as "PhD Position for Recommendation System"

For project specific queries, please contact: Dr. Azadeh Mohammadi,   

Email address: [Email Address Removed]