Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance

Emanuel Lacic, Dominik Kowald

Research output: Chapter in Book/Report/Conference proceedingConference paper


Recommender systems have gained a tremendous increase in popularity in recent years for many industry practitioners. Early recommender systems often considered only user-item interactions, but nowadays, many application domains can leverage different contextual sources like textual meta-data, images or implicitly arising graph structures. Furthermore, practitioners who build modern recommender systems need to address the scalability and real-time demand when providing recommendations in an online setting, since there is usually a trade-off between accuracy and runtime performance. When put into production, different challenges need to be addressed in order to continuously maintain the stability and health of a recommender system. A distributed architecture which is guided by design principles like providing service isolation, supporting data heterogeneity, allowing for algorithmic customization as well as ensuring fault tolerance is thus a necessity.

In this talk, we will show how to build a modern recommender system that can serve recommendations in real-time for a diverse set of application domains. We will share our experiences that we gained in both research-oriented (e.g., Horizon 2020) and industry-oriented projects on how we build hybrid models based on a microservice architecture. This architecture utilizes popular algorithms from the literature such as Collaborative Filtering, Content-based Filtering as well as various neural embedding approaches (e.g., Doc2Vec, Autoencoders, etc.). We will further show how we adapt our architecture to calculate relevant recommendations in real-time (i.e., after a recommendation is requested), since in many cases individual requests may be targeted for user sessions that are short-lived and context-dependent.

To showcase the applicability of such an approach, we will specifically focus on and present two real-world use-cases, namely providing recommendations for the domains of (i) job marketplaces, and (ii) entrepreneurial start-up founding. For the former, we tackle the problem of finding the right job for university students by guiding the students toward different types of entities that are related to their career, i.e., job postings, company profiles, and career-related articles. Here, for instance, we find that the online performance of the utilized approach also depends on the location context where the recommendations are displayed. For the latter, we will present how a recommender system can support academic entrepreneurs who want to go through the process of building a start-up from an initial innovation idea. In such a setting, a recommender system needs to suggest relevant experts that can provide feedback to an innovation idea, support potential co-founder and team member matching, allow accelerators, incubators, and innovation hubs to discover these innovations as well as continuously provide relevant education materials until the innovation idea has become mature enough in order to form a start-up. By adapting a recommender system for such diverse personalization scenarios, we observe that a dynamic customization of the utilized recommender algorithms with respect to the underlying data structures is of key importance.

Taken together, we strongly believe that our experiences from both research- and industry-oriented projects should be of interest for the ECIR audience, especially for practitioners in the field of real-time multi-domain recommender systems.
Original languageEnglish
Title of host publicationIndustry-Day Track of European Conference on Information Retrieval
Publication statusPublished - 11 Apr 2022
Event44th European Conference on Information Retrieval: ECIR 2022 - Stavanger, Norway
Duration: 10 Apr 202214 Apr 2022


Conference44th European Conference on Information Retrieval
Abbreviated titleECIR 2022

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