{"type":"ScholarlyArticle","title":"Proposing a Highly Accurate Hybrid Component-Based Factorised Preference Model in Recommender Systems","authors":[{"name":"Farhad Zafari","slug":"farhad-zafari","given":"Farhad","family":"Zafari","affiliation":"Swinburne University of Technology , Melbourne , VIC 3122 , Australia"},{"name":"Rasoul Rahmani","slug":"rasoul-rahmani","given":"Rasoul","family":"Rahmani","affiliation":"Swinburne University of Technology , Melbourne , VIC 3122 , Australia"},{"name":"Irene Moser","slug":"irene-moser","given":"Irene","family":"Moser","affiliation":"Swinburne University of Technology , Melbourne , VIC 3122 , Australia"}],"abstract":"Recommender systems play an important role in today's electronic markets due to the large benefits they bring by helping businesses understand their customers' needs and preferences. The major preference components modelled by current recommender systems include user and item biases, feature value preferences, conditional dependencies, temporal preference drifts, and social influence on preferences. In this paper, we introduce a new hybrid latent factor model that achieves great accuracy by integrating all these preference components in a unified model efficiently. The proposed model employs gradient descent to optimise the model parameters, and an evolutionary algorithm to optimise the hyper-parameters and gradient descent learning rates. Using two popular datasets, we investigate the interaction effects of the preference components with each other.We conclude that depending on the dataset, different interactions exist between the preference components. Therefore, understanding these interaction effects is crucial in designing an accurate preference model in every preference dataset and domain.Our results show that on both datasets, different combinations of components result in different accuracies of recommendation, suggesting that some parts of the model interact strongly. Moreover, these effects are highly dataset-dependent, suggesting the need for exploring these effects before choosing the appropriate combination of components.","doi":"10.24963/ijcai.2017/185","url":"https://ijcai.org/proceedings/2017/185","pdf":"https://ijcai.org/proceedings/2017/0185.pdf","markdown":"https://ijcai.org/proceedings/2017/185.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/185","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Knowledge Representation, Reasoning, and Logic","track":"Main track","number":185,"pages":{"first":1332,"last":1339},"keywords":["Machine Learning: Learning Preferences or Rankings","Knowledge Representation, Reasoning, and Logic: Preferences","Knowledge Representation, Reasoning, and Logic: Preference modelling and preference-based reasoning"],"videos":[],"references":[{"position":1,"raw":"[Aghdam et al., 2015] Mehdi Hosseinzadeh Aghdam, Morteza Analoui, and Peyman Kabiri. 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