Improving Sequential Recommendations with LLMs

The sequential recommendation problem has attracted considerable research attention in the past few years, leading to the rise of numerous recommendation models. 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 design three orthogonal approaches and hybrids of those to leverage the power of LLMs in different ways. In addition, we investigate the potential of each approach by focusing on its comprising technical aspects and determining an array of alternative choices for each one. We conduct extensive experiments on three datasets and explore a large variety of configurations, including different language models and baseline recommendation models, to obtain a comprehensive picture of the performance of each approach. Among other observations, we highlight that initializing state-of-the-art sequential recommendation models such as BERT4Rec or SASRec with embeddings obtained from an LLM can lead to substantial performance gains in terms of accuracy. Furthermore, we find that fine-tuning an LLM for recommendation tasks enables it to learn not only the tasks, but also concepts of a domain to some extent. We also show that fine-tuning OpenAI GPT leads to considerably better performance than fine-tuning Google PaLM 2. Overall, our extensive experiments indicate a huge potential value of leveraging LLMs in future recommendation approaches. We publicly share the code and data of our experiments to ensure reproducibility.

Machine Learning for Software Engineering: A Tertiary Study

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009-2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches.