Nowadays, with the abundance of the internet content, users expect the recommendation platforms to not only help them with one-off decisions and short-term tasks, but to also support their persistent and overarching interest journeys, including their real-life goals that last days, months or even years. In order for recommender systems to truly assist users through their real-life journeys, they need to first be able to understand and reason about interests, needs, and goals users want to pursue; and then plan taking those into account. However, the task presents several challenges. In this talk, we will present the key steps and elements needed to tackle the problem — particularly (1) user research for interest journeys; (2) personalized and interpretable user profiles; (3) adapting large language models, and other foundational models, for better user understanding; (4) better planning at a macro-level through reinforcement learning and reason-and-act conversational agents; (5) novel journey-powered front end user experiences, allowing for more user control. We hope that the talk will help inspire other researchers, and will pave the way towards companion recommenders that can truly assist the users throughout their interest journeys.