# Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System
## 2 Related Work
\begin{tabular}{c} **Content-** \\ **Based** \\ **RS** \\ \end{tabular}  & \begin{tabular}{c} **Collab.** \\ **RS** \\ \end{tabular}  & \begin{tabular}{c} **Demog.** \\ **Based** \\ **RS** \\ \end{tabular}  & \begin{tabular}{c} **Knowl.** \\ **Based** \\ **RS** \\ \end{tabular}  & \begin{tabular}{c} **Commun.** \\ **Based** \\ **RS** \\ \end{tabular}  & \ \begin{tabular}{c} **Hybrid** \\ **RS** \\ \end{tabular}  & \begin{tabular}{c} **Cold-** \\ **Start** \\ **RS** \\ \end{tabular}  \\ \hline
[14] & x & & & & & & & & \\
[15] & x & & & & & & & \\
[16] & & x & & & & & & \\
[17] & & & x & & & & & \\
[18] & & & & & x & & & \\
[19]  
Ref. [22], who presented a cascade-hybrid RS composed of a content-based recommender followed by a user-based or item-based collaborative RS, depending on whether the user is considered high or low interaction, respectively. This study demonstrated the importance of combining various methods to achieve a satisfactory analysis, while emphasising the necessity of developing models focused on the cold-start cases. Typically adopted evaluation metrics, prevalent in the presented articles, include the root mean square error, regarding the error associated with the predicted rating [16; 19; 20; 22], the precision, reflecting the percentage of relevant items recommended from list of recommended items [14; 16], the recall, regarding the percentage of relevant items recommended from list of relevant items [21], the hit rate, indicating the percentage of recommendations where one item was relevant [21] and the coverage, reflecting the percentage of the total items that were recommended by the system [14; 22]. The use of these metrics allows for the comparison of different models and adjustment of hyper parameters during the model definition phase, assessing performance. Cold-start situations heavily impact the performance of RS, leading to a necessity of developing models adapted to these cases. Ref. [9] defined the cold-start problem for two situations: cold-start for new users or new items. The case to be tackled in this paper is the recent user cold-start, regarding the existence of insufficient information and interactions associated with certain users. Within marketing, this issue happens when customers have not made enough purchases or interactions with the brand to enable the extraction of relevant information about the user. Cold-start RS, although similar to a typical RS, require additional complexity in order to overcome the lack of data. For example, the use of feature engineering enables the increase of the number of inputs to the model [25], while segmentation can aid in behaviour prediction [26], thus increasing performance. Furthermore, exploring additional ML tools can further expand the potential of RS [27]. For example, [PERSON]. [28] proposed the use of a multi clustering recommendation system to predict new customer behaviour. The approach starts by clustering items, followed by a customer feature engineering, allowing the segmentation of users and finalising with item recommendations. The algorithm was proved to increase buyers' attention and addressed the issue of cold-start through the prediction of new users' behaviour. Regarding the application of ML tools, [PERSON]. [23] used a multi-level fuzzy RS, analysing several continuous inputs in order to make inferences regarding the similarity between users. This system was found to be valuable for extracting more information from cold-start cases. Additionally, [PERSON]. [24] suggested the inclusion of Market Basket Analysis (MBA) [33] using pairwise association rules, resulting in a RS independent of complex user and item information. This approach identifies related items based on the behaviour of a population of users, building a collective model of preferences. This circumvents the cold-start problematic, preventing biased recommendations. ColdGAN [29] is a GAN-based recommendation system that addresses the problem of new user cold-start recommendation.