While most organizations produce large amounts of data, very few are able to use that information to generate value. According to Harvard Business Review, recommender systems are the single most important distinction between “born digital” companies and legacy companies.
Although users have long been familiar with recommendation systems on B2C platforms, the requirements are substantially different in B2B, where it is a matter of adding value to existing content, assisting in the creation of new content, and linking, comparing, and contextualizing the content repositories.
While there are various types of B2B recommender systems on the market, knowledge-based recommender systems are proving to be a reliable source, especially in the face of Generative AI. Our knowledge-based recommender systems combine various AI techniques with subject matter expertise to provide comprehensive knowledge about a subject or task. These systems follow the human-in-the-loop principle to ensure that decisions are justified and trustworthy.
Which recommender systems work best for corporate use?
In a company, recommendation systems can only be helpful if they are tailored to specific use cases, fit the requirements of employees, and comply with industry standards. The technological approach of a recommender system is therefore crucial. We believe that knowledge-based recommender systems offer the best value for the most appropriate corporate set of features.
Based on “A comparative analysis of different recommender systems” by Christine Lahoud, Sherin Moussa, Charbel Obeid, Hicham El Khoury, Pierre-Antoine Champin at Springer Nature 2022
PoolParty Recommender Systems – A Catalyst for Value Creation
Enterprise recommender systems are intended to serve as a catalyst for additional value creation wherever relevant knowledge creates measurable benefits on a monthly basis. Be it the identification of risks in the design process, the triggering of alarms in borderline situations of the supply chain, the observation of market and legislation, or even the suggestion of the right document for the corresponding application. Recommendation systems therefore stand for value and efficiency assurances along the entire operational value chain.
Built for you!
Out-of-the-box solutions quickly reach their limits and it can happen that your recommendation system does not generate the added value you expect. The PoolParty recommender system is as flexible as your company’s needs are. We have been integrating knowledge systems for companies for over 20 years, so our systems are prepared for your specific circumstances by design.
The Advanced Assistant
Semantic recommender systems are driven by domain, context, and intent, which means that they are highly customizable to an organization’s specific use case. While other systems cannot function as the “knowing assistant” to the user, the PoolParty Recommender can make knowledge available that makes a difference and adds value.
Your Plug-in-style Recommender System!
While other systems require a company’s knowledge management to be built from scratch, the PoolParty recommendation system leaves the company’s internal knowledge management intact as basis for implementation and training. A usually existing taxonomy, knowledge graph, or corpus can serve as the foundation on which the PoolParty recommender system is built. The recommender can be operated by existing personnel.
A knowledge-based recommender system utilizing semantic footprints
In essence, the knowledge-based approach to recommender systems assumes that each knowledge object can be given a unique position in the knowledge graph. We call this semantic footprinting. This position represents the combination of inherent content description and contextualization within the knowledge space. This combination is the core element of the recommender system, which maps usable relations between content objects independent of content type and content usage (user behavior).
Traceable recommendation workflow of the PoolParty recommendation system
While other systems go through untraceable processes, knowledge-based recommendation systems follow a clearly defined and step-by-step explainable and transparent logic. The user submits the query (1), which consists of a sentence, a paragraph, a section, or an entire document. The text passes through the text annotation component (2) and receives its semantic footprint (3). The subsequent query expansion (4) is a traceable intervention in recommendation depth and recommendation sharpness. The matches found (5) thus contain both the implicit knowledge of the domain model and the query-specific adjustments. The finally obtained recommendation (6) is clearly traceable and controlled.
Thus, the PoolParty approach for recommender systems provides us with high domain specificity and adaptability to the particular application domains. It delivers relevant results since it has no blind spots in filtering behavior. Moreover, with the adaptable architecture that PoolParty recommender systems offer, we can combine knowledge graph entities with statistical AI implementations (ML, LLMs), thus combining capabilities from both worlds.