# New Hybrid Techniques for Business Recommender Systems
## 2 Related Work
### Business-Oriented Recommendations  
With regard to recommender algorithms, business-oriented recommender systems have to deal with **complexity** in terms of company contexts (input) and solutions (output). Attempts to deal with such complexity can be divided into several categories:  
* _Augmentations of content-based filtering:_ Approaches in this category model both the input and output complexities and establish the degree to which both of them match. For instance, constraint-based recommenders [4; 16] help model product features and constraints to be expressed about them and then ensure constraint satisfaction. Other approaches use tree-like structures to model items and user preferences [17] or use multiple levels on which queries and items are matched (such as recommending first providers and then actual services in a service recommender [18]). In content-based filtering, additional knowledge can be incorporated, e.g., into the function that determines the similarity between an item and the user profile. Often, this is knowledge about user context, item features and/or domain-specific constraints. For instance, refs. [19; 20] use ontologies to represent and reason about item features and to apply this knowledge in a sophisticated similarity measure that takes into account \"hidden relationships\" [20]. [PERSON] et al. [21] use an ontology to represent user profiles and engage users in correcting the profiles before assessing profile-item similarities. The complexity of business contexts has also been highlighted in [22], where the authors focus on identifying the criteria for recommendations in business processes that will serve as inputs to knowledge-based recommenders. * _Augmentations of collaborative filtering:_ Case-based recommenders [6; 23] can be seen as a special form of collaborative filtering since they recommend items used in solutions of companies that are similar to the current company. However, instead of only considering already chosen items, case based recommenders' similarity measures take into account context variables that describe, e.g., company demographics and other relevant aspects of the company's problem and/or initial situation. Since case-based reasoning is an approach based on problem-solving from past experience, case-based recommenders have been implemented in domains that most benefit from contextual information coming from past experience. For example, [24] explored case-based recommenders to recommend personalized financial products to the customers of a banking organisation. The authors of [25] argue that case-based recommenders are much more suitable for a complex domain of smart-city initiatives as they can utilize a rich range of domain-specific attributes. * _Graph-based recommenders:_ Recommender algorithms based on graph structures [7; 26; 27] have been put forward because of their ability to accommodate a wide variety of forms of contexts in a flexible way without much effort. Random walks [28; 29; 30] are a predominant type of algorithm to provide recommendations based on graph structures. Because of their simplicity, graphs also have limitations, e.g., in modeling and matching simple string-valued attributes of input cases or in modeling certain forms of complex solution structures. The possibility of using graph-based recommenders to \"mimick\" traditional recommender approaches, such as collaborative or content-based filtering, has been explored by [PERSON] et al. [31]. For this, one needs to assign different weight to different types of graph relations. A comprehensive study by [32] has identified the potential of knowledge graphs to improve the explanability of recommendations as well as to provide dynamic recommendations. [PERSON] et al. [33] have combined knowledge graphs with content-based filtering to support the recruitment of consultants with suitable skills for clients by calculating semantic similarity between the graph nodes.