Contextual information is a prerequisite for timely offering of personalized decision support and recommendation. Yet, research on context-aware recommender systems (CARS) does not appear to be thriving, and finding public datasets containing context factors is a challenging task. We can make various assumptions about why this drop in research interest happened – be it ethical considerations or the popularity of opaque deep learning models that merely consider context in an implicit way. This is an unwelcome development. We argue that continued effort must be put on the creation of suitable datasets. Furthermore, we see significant opportunities in the development of next-generation CARS in the space of interactive AI assistants powered by Large Language Models.@en

Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches. In this work, we explore how large language models (LLMs), which are nowadays introducing disruptive effects in many AI-based applications, can be used to build or improve sequential recommendation approaches. Specifically, we devise and evaluate three approaches to leverage the power of LLMs in different ways. Our results from experiments on two datasets show that initializing the state-of-the-art sequential recommendation model BERT4Rec with embeddings obtained from an LLM improves NDCG by 15-20% compared to the vanilla BERT4Rec model. Furthermore, we find that a simple approach that leverages LLM embeddings for producing recommendations, can provide competitive performance by highlighting semantically related items. We publicly share the code and data of our experiments to ensure reproducibility.1

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Research on understanding, developing and assessing personalisation systems is spread over multiple disciplines and builds on methodologies and findings from several different research fields and traditions, such as Artificial Intelligence (AI), Machine Learning (ML), Human–Computer Interaction (HCI), and User Modelling based on (applied) social and cognitive psychology. The fields of AI and ML primarily focus on the optimisation of personalisation applications, and concentrate on creating ever more accurate algorithmic decision makers and prediction models. In the fields of HCI and Information Systems, scholars are primarily interested in the phenomena around the use and interaction with personalisation systems, while Cognitive Science (partly) delivers the theoretical underpinnings for the observed effects. The aim and contribution of this work is to put together the pieces about the impact of personalisation and recommendation systems from these different backgrounds in order to formulate a research agenda and provide a perspective on future developments.

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