Recommender system based on pairwise association rules
| Autor: | Maisie K. Rowland, Timur Osadchiy, Emma Foster, Patrick Olivier, Ivan Poliakov |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Information retrieval Association rule learning Computer science General Engineering 02 engineering and technology Recommender system Ontology (information science) Computer Science Applications Set (abstract data type) Range (mathematics) 020901 industrial engineering & automation Cold start Artificial Intelligence Face (geometry) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pairwise comparison Transaction data |
| Zdroj: | Expert Systems with Applications. 115:535-542 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2018.07.077 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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