Project Outline and Aims

Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, applications such as travel planning can benefit from a system capable of recommending packages of items in the form of sets or sequences, and within a user-specified budget. In this context, it is useful if the system can recommend the top-k packages for the user to choose from. Motivated by these applications, we are studying composite recommendations, where each recommendation comprises a set or sequence of items. Each item has both a value (rating) and a cost associated with it, and the user specifies a maximum total cost (budget) for any composite recommendation. Because the problem of generating the top recommendation (package) is NP-complete, we are studying approximation algorithms for generating top-k composite recommendations.

Funding and Staffing Details

The project is being undertaken by Peter Wood, Laks Lakshmanan (UBC) and Min Xie (UBC).

Project publications