User Buddy: Minimizing Regret

It's like your less annoying virtual partner.

User Buddies - The Bridge Between Worlds

User Buddies serve as personal ambassadors in the MetaSpace, translating human curiosity into actionable discovery. Unlike traditional recommendation systems that simply match patterns, User Buddies develop a deep contextual understanding of their human partners through continuous interaction and preference learning.

The objective of user buddies is minimizing their regret of missing a better Goal Buddy candidate by learning a better retrieval model.

$$\mathcal{U}_{\text{user}} = \min_{\pi} \mathcal{R}(S, q) \quad \text{where} \quad \mathcal{R}(S, q) = \sum_t \left[ \max_{e_i\in C} e_i \cdot q - \sum_{e_i \in S} e_i \cdot q \right]$$

$S$: candidate set is constructed by retrieving top-k embedding, $\text{top-k}_{e_\in C}(q \cdot e)$, that are close to user's preference vector $q$$\text{top-k}_{e_\in C}(q \cdot e)$. User Buddies navigate the MetaSpace by interfacing with Goal Buddies through vectorized content retrieval. They don't just filter existing content—they actively engage with Goal Buddies to tailor insights to their user's evolving interests. When a user expresses interest in Bitcoin, their User Buddy doesn't just fetch articles—it initiates dialogues with the Bitcoin Buddy to generate personalized insights.

$q$: user preference is also a dynamic vector reflecting user's dynamic preference over time. By maintaining a preference memory that captures both explicit feedback and implicit signals, they learn not just what users like, but what they wish they had discovered sooner. This creates a feedback loop that continuously refines their understanding of user aspirations.

User Buddies transcend the role of passive recommendation filters to become active partners in discovery. They are the vital bridge between human curiosity and the collective intelligence of Goal Buddies, ensuring that the vast knowledge of the MetaSpace remains personally meaningful and actionable.