# New Hybrid Techniques for Business Recommender Systems
## 1 Introduction  
Digitalisation leads to a transformation of internal business processes but also very notably of customer-facing services, while most attention is paid to services in the B2C domain, there is also a rising interest in digitalising knowledge-intensive services in the B2B domain, such as consultancy in general [1] and IT consultancy in particular [2]. Such transformation implies that a digital service (partially) takes over the role of a human consultant and that companies can use that service to help themselves to the required advice. Obviously, such digital services will be able to give advice only for restricted domains; often, advice will consist of recommending items from a predefined set of solution components. Thus, digital consulting services can be thought of as recommender systems. As we have laid out in our previous work [3], a recommender that suggests solution components to companies is different in several ways from the typical B2C recommenders that help users in finding, e.g., books, movies or music that fit their preferences (see also [4]):  
* Requirement-driven: A consultancy recommender needs to consider business requirements not personal preferences;
* Interdependent items: The recommended items are not simple, atomic and independent products (such as books, movies etc.) but interdependent and sometimes complex components of a larger solution;
* No profiles: While typical B2C recommenders are used repeatedly by the same person, a digital consultancy service has no chance to build up customer profiles through repeated interactions; companies will usually access the service only once. Thus, a profile of the company needs to be acquired within a single session by the recommender; one can regard it as forming a _query_ that describes the situation of the company seeking advice. Despite some of these differences, one can establish a \"digital consultancy process\" that will make it possible to apply traditional recommender techniques that have beendesigned for classical preference-based B2C scenarios. Such a process is based on the following considerations (see also Figure 1):  
1. Many companies share the same requirements, just like many persons share preferences. The similarity of requirements often depends on the companies' demographics (e.g., size, industry etc.). Thus, a first step in the digital consultancy process may be to capture company demographics and regard them as an initial company profile or **initial query**. This allows establishing a certain similarity between companies from the beginning. 2. Later, the similarity of context and requirements manifests itself in accepting similar suggestions from the recommender. Since solutions will be complex, one may construct a repeated interaction with the recommender in the form of iterations: after entering the company demographics (step 1), the business user receives a first set of recommendations and selects from those some first elements of a solution. These elements are added to the initial company profile to form an **extended query**, and the recommender is invoked again. This process is repeated, each time with a more verbose query. (We will later use the term \"query verbosity\" to refer to the growing amount of information that the query contains.) Following this iterative process will allow us to assess the _similarity_ of company contexts by comparing queries of a company to those of previous users of the service, with an increasing degree of accuracy as the query is iteratively extended. Since similarity is at the heart of both content-based and collaborative filtering approaches [5], being able to assess similarities is an important prerequisite for applying these approaches. In addition, we build up company profiles during the process, which makes it possible to apply content-based filtering. The iterative refinement also makes it possible to take into account the interdependence of solution elements by identifying, in each new step, new elements that fit to the already selected elements.