Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
11002299 | Expert Systems with Applications | 2019 | 30 Pages |
Abstract
Recommender systems based on methods such as collaborative and content-based filtering rely on extensive user profiles and item descriptors as well as on an extensive history of user preferences. Such methods face a number of challenges; including the cold-start problem in systems characterized by irregular usage, privacy concerns, and contexts where the range of indicators representing user interests is limited. We describe a recommender algorithm that builds a model of collective preferences independently of personal user interests and does not require a complex system of ratings. The performance of the algorithm is analyzed on a large transactional data set generated by a real-world dietary intake recall system.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Timur Osadchiy, Ivan Poliakov, Patrick Olivier, Maisie Rowland, Emma Foster,