# Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System
## 1 Introduction  
Digital marketing is the result of an era of information and technology. New opportunities bring along responsibilities; the ability to reach the world within a few clicks leads to a need to do so in a timely and relevant manner, respecting regulations such as the General Data Protection Regulation (GDPR), privacy and human rights [1]. Thus, there is an ever increasing demand for more relevant, personalised, engaging and entertaining methods of marketing. Simultaneously, the trend has been to automatise and simplify these methods, delegating analytics to machines and freeing human resources to more creative tasks [2; 3; 4].  
Machine Learning (ML) positions itself as a valuable tool able to extract information from large amounts of data, thriving from volume, velocity, variety and veracity, inherent characteristics of big data [5]. ML is capable of exceeding human capacity in several tasks, being extremely adaptable and fast responding, often used for classification, regression, and prediction tasks [4; 6].  
Within the spectrum of ML applications for digital marketing, recommender systems (RS) exhibit great utility and potential [7]. These systems seek to understand the connection between users and items, identifying patterns among them in order to predict user preferences [8]. By obtaining this information through complex models, RS provide relevant recommendations, playing a role towards responsible, functional, pertinent and non-invasive marketing.  
RS rely on the quantity and quality of data, since a lack of information disrupts the analytical power of ML tools. With an increased challenge of providing suitable recommendations, the cold-start issue arises [9]. Cold-start is defined by the absence of sufficient data to fulfil model requirements, either regarding user or item data. Considering the digital marketing scenario, cold-start often occurs in regard to recent customers and products. When presented with a full non-existence of data, the case is referred to as frozen-start [10]. The frozen-start case distinguishes itself from the exclusively cold-start case due to the inability of developing customized recommendations, as there is no characterising feature.  
Trends within the area of digital marketing greatly impact RS's requirements. Ref. [11] explored the concept of hyper-relevance within marketing, predicting _\"(...) a post-marketing utopia where old forms of marketing--the disruptive, irrelevant, and corporate kind--have been replaced by something new and different, still performed by marketers but no longer marketing\"_. Hyper-relevance reinforces the challenge of treating cold-start cases, as achieving accurate understanding and assuming a relevant role becomes increasingly difficult with the lack of data.  
This paper's focal point is to provide recommendations to cold-start users given the challenge and potential this area presents. It is required to perceive the users' main distinguishing features, identifying connections between these characteristics and user preferences. The user cold-start limitation is typically evaded through the use of extensive item information and explicit ratings; the developed RS distinguishes itself from the current state of the art due to utilizing a limited set of item and transactional information. Considering the paper's objectives, frozen-start cases will be excluded from the analysis due to the inability to provide customisation.  
This paper is organised as follows: Section 2 presents the latest developments within the area of digital marketing, followed by a review of recommender systems and concluding with a focus on the cold-start problem. Section 3 explores the problem and data description. Section 4 describes the adopted methodology in detail. Section 5 presents the obtained results, providing a comparison and critical analysis. Finally, Section 6 presents the main conclusions and opportunities for future work.