Serendipity and LLM-based Recommender System for Smart Transportation

Serendipity-oriented recommender system facilitates surprise encounters to the users. In the field of smart transportation. It's therefore important to investigate how recent developments in large language models (LLM) when integrated in smart transportation can help commuters use serendipity of recommendation to their advantage. This study involves user study of a recommendations experience of large language models and understands the serendipity facilitating aspects of large language models. The study collected feedback from 48 users of LLM-based chatbot. The study revealed the potential and useful applications of LLM, facilitating serendipity to commuters in the context of smart transportation. LLM's aspect of facilitating serendipity and recommending items to users. It also revealed that LLM based sysetm have serendipity facilitating potential will not only benefit commuters but to a large audience of smart transportation users. Therefore, its recommended to develop LLM based serendipity facilitating recommender systems for smart transportation.