Abstract
Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the ever-increasing number of choices available. However, most current systems ignore the fact that user preferences can change according to context, resulting in recommendations that do not fit user interests. This research addresses these issues by proposing the ({ CF})^2 architecture, which uses local learning techniques to embed contextual awareness into collaborative filtering models. The proposed architecture is demonstrated on two large-scale case studies involving over 130 million and over 7 million unique samples, respectively. Results show that contextual models trained with a small fraction of the data provided similar accuracy to collaborative filtering models trained with the complete dataset. Moreover, the impact of taking into account context in real-world datasets has been demonstrated by higher accuracy of context-based models in comparison to random selection models.
Highlights
In everyday life, it is not uncommon to rely on recommendations by friends or family to decide on a restaurant for dinner
The presented experiments show that the (CF)2 architecture achieves similar accuracy using a small fraction of the data to collaborative filtering models trained with the complete dataset
The work described in this paper has developed the (CF)2 architecture, which uses local learning techniques to embed contextual awareness into Collaborative filteringCollaborative filtering (CF) models
Summary
It is not uncommon to rely on recommendations by friends or family to decide on a restaurant for dinner. Collaborative recommendation resembles word-ofmouth communication, in which the opinions of others are used to determine the relevance of a recommendation. In this case, a collaborative recommender system uses the ratings provided by its users either to recommend an interesting item or to identify like-minded users. CF provides recommendations based on the opinions of others who share the same interests as the user (Lu et al 2015). These opinions are often represented as the ratings matrix R (Aggarwal 2016b). Because in any recommender system the number of ratings obtained is usually very small compared to the number of users × items , the matrix R is often sparse (Bindu et al 2017)
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