  Recommender systems (RS) are vital for managing information overload and
delivering personalized content, responding to users' diverse information
needs. The emergence of large language models (LLMs) offers a new horizon for
redefining recommender systems with vast general knowledge and reasoning
capabilities. Standing across this LLM era, we aim to integrate recommender
systems into a broader picture, and pave the way for more comprehensive
solutions for future research. Therefore, we first offer a comprehensive
overview of the technical progression of recommender systems, particularly
focusing on language foundation models and their applications in
recommendation. We identify two evolution paths of modern recommender systems
-- via list-wise recommendation and conversational recommendation. These two
paths finally converge at LLM agents with superior capabilities of long-term
memory, reflection, and tool intelligence. Along these two paths, we point out
that the information effectiveness of the recommendation is increased, while
the user's acquisition cost is decreased. Technical features, research
methodologies, and inherent challenges for each milestone along the path are
carefully investigated -- from traditional list-wise recommendation to
LLM-enhanced recommendation to recommendation with LLM agents. Finally, we
highlight several unresolved challenges crucial for the development of future
personalization technologies and interfaces and discuss the future prospects.
