Accurate, efficient, and explainable modelling of context-dependent preferences using matrix factorisation

Nowadays, consumers are overwhelmed with a large number of products/services available on the web, and recommender systems help them choose the right products/services to buy. They also help businesses match their products/services to the right consumers and hence increase their profits. A dilemma faced in preference modelling in recommender systems is whether to choose an understandable/explainable simple system while sacrificing accuracy, or an accurate complex model while sacrificing explainability. According to our investigations, consumer preferences are generally comprised of several aspects. These aspects describe a user’s preferences over the different options, their characteristics and the dependencies between the characteristics, the influence of social relationships on preferences, the temporal properties of preferences, and the biases users have when specifying preferences on options. A general problem is how to model these aspects efficiently using an accurate model that can be easily explained to users and businesses. The explainability of recommendations is important, because explanations to users have been shown to improve the system transparency and usability. Although each of the preference aspects has been investigated in isolation, there is currently no general architecture that unifies these aspects into a single model. Developing such a multi-aspect architecture not only helps us achieve a high recommendation quality, but also potentially enables us to provide intuitive reasons for the recommendations. This research makes several important contributions to the field. Hidden aspects in consumer feedback are used to make more accurate recommendations to the users. One aspect is how the users perceive the items in terms of the features they have. We show the importance of capturing these features in the latent factor models, and how these models can overcome the degrading effects of using large numbers of factors. Other aspects relate to the social influence and temporal properties of preferences. We model the social influence on all aspects of preferences, and capture the drifting behavior of aspects over time. We make several assumptions regarding the mathematical properties of these aspects. These assumptions enable us to efficiently capture all the hidden preference aspects, their social iii and temporal properties, and interactions. The model strikes a balance between efficiency and accuracy and achieves higher accuracies than previous models. The preference aspects are captured in an integrated component-based architecture, where each aspect corresponds to a component. The component-based design permits us to consider the interplay between various preference aspects and the domain-dependent interaction effects between them. This component-based architecture facilitates making more accurate recommendations by considering interactions. It also enhances explainability, since each component captures one meaningful aspect that can be explained to the users. The intuitive and comprehensive explanations to the users and businesses regarding the preference aspects are provided for both businesses and consumers. The meaning of explanations is also enhanced by using the verbal reviews by the users to improve the understandability.