## 2 Related Work
### The Product Assortment and Product Recommendation Problems
They adopted an intuitive co-clustering algorithm for locating useful patterns in a 0-1 matrix, which studies the buying behavior of customers using historical data on past purchases. In order to handle the product recommendation problem for e-commerce applications, [PERSON] et al. [34] proposed a co-operation framework for multiple role-based reasoning agents. [PERSON] and [PERSON] [35] presented a similar product-finding algorithm for the collaborative business companies that share a product taxonomy table and have exchangeable product information. [PERSON] et al. [36] proposed an online product recommendation system, which combines implicit rating-based collaborative filtering (CF) and sequential pattern analysis (SPA). The system derives implicit ratings by applying CF to online transaction data--even when no explicit rating information is available--and integrates CF and SPA for improving recommendation quality. [PERSON] et al. [37] developed a novel product recommender system called METIS. Their system identifies, almost in real-time, users' purchase trials from their microblogs. Then, it makes product recommendations based on matching the users' demographic information--extracted from their public profiles--with product demographics learned from these microblogs and additional online reviews. [PERSON] et al. [38] proposed a novel solution for 'cross-site' and 'cold-start' product recommendation, which recommends products from e-commerce websites to users at social networking sites in cold-start situations. The term cold-start refers to users who do not have historical records on the items they have purchased. The authors proposed learning both users' and product feature representations via data collected from e-commerce websites, using recurrent neural networks and then applying a modified gradient boosting trees method to transform users' social networking features into user purchase preferences. More recently, [PERSON] et al. [16] addressed a particular product recommendation problem regarding small-scale retail websites, where the small number of returning customers makes traditional user-centric personalization techniques inapplicable. Hence, these authors applied an item-centric product recommendation strategy that combines two well-known methods--association rules and text-based similarity--for generating recommendations based on a single'seed' product. Furthermore, their approach was also used to recommend products based on a set of seed products in a user's shopping basket. The effectiveness of their recommendation approach is demonstrated, in the product-seeded and basket-seeded scenarios, through a series of experiments employing real customer data. Product recommendation systems are related to the product assortment problem: a set of correlated products must be selected to be exposed (or recommended) in an exposition area with limited capacity. This selection of products should help to improve the experience of customers when visiting a store. In effect, by exposing an appropriate set of items at the display tables it is possible to increase the level of attraction of customers to the store, which directly influences the customers' experience and, hence, the sales revenue. Although this Section shows that research has been undertaken on both product recommendation systems and the product assortment problem, there is still a lack of work on how to combine different products in retail display tables at brick-and-mortar stores, especially when considering a multi-period time horizon and dynamic attractiveness values.