Adaptive Personalisation for Effective Conversational Search Systems

Conversational Information Seeking (CIS) requires systems to maintain context across turns, resolve referential ambiguities, and adapt to evolving user intent [6].Beyond contextual understanding, truly effective CIS demands personalisation: adapting retrieval and responses to each user's unique background and preferences [2].However, two fundamental limitations constrain current systems.First, existing conversational search pipelines apply a single static workflow to every query, lacking the adaptability to decide when personalisation helps and when it degrades retrieval precision.Second, personalisation in standard benchmarks typically relies on clean, hand-authored oracle user profiles, ignoring the reality that real-world user context is buried within noisy, long-term interaction logs.This research addresses both limitations by proposing a novel modular, agentic framework, realised through a suite of specialised agents that are coordinated by a dynamic orchestration mechanism.The first completed contribution (RQ1) addresses the granularity of personalisation during query reformulation.We introduced a new set of human annotations for the iKAT 2023 test collection to capture the specific personalisation requirements of queries.Leveraging this data, we developed an adaptive framework with a query-level classifier that determines whether the retrieval quality of a given conversational query can be improved by including the user profile.Consequently, queries are selectively routed between a general and a personalised reformulation path, preventing profile noise from degrading performance on simple information needs [4].The second completed contribution (RQ2) extended this adaptive personalisation ability to the ranking phase.We developed an adaptive reranking framework that dynamically routes queries between standard similarity-based rerankers and specialised instructionfollowing rerankers based on conversational intent [3].When complex, user-specific constraints are detected, the system routes the retrieved documents to an instruction-following reranker.This reranker reorders the documents according to nuanced, profilederived instructions, bridging the gap between raw retrieval and generation-ready contexts [5].Current work (RQ3) targets the oracle-profile assumption.Rather than relying on static, pre-defined user profiles assumed at test time, we propose an agentic long-term memory component that dynamically builds and maintains personalised user profiles from noisy historical interaction logs.We evaluate this component using the