Dynamic Information FilteringPh动态信息过滤PH值.D. Thesis Patrick.pptVIP

Dynamic information filtering Patrick Baudisch Xerox PARC March 26, 2001 Contents Introduction Requirements and related work The TV Scout …as a retrieval system …and as a filtering system How it works The QuerySet Architecture Building QuerySet filtering systems Manual profile editing Conclusions Introduction Requirements and related work The TV Scout …as a retrieval system …and as a filtering system How it works The QuerySet Architecture Building QuerySet filtering systems Manual profile editing Conclusions Motivation: Information overload Too many research papers books movies web pages … even TV programs! Goal: alleviate information overload IF, IR, and dynamic filtering Analytic information seeking strategies Retrieval (IR) changing interests, stable database Filtering (IF) changing sources, stable interests Many application fit in dictionaries = IR music = IF Others fit into neither niche High source and need change rate Example stock market [Oard 96]: “Grand challenge” Objective of dynamic filtering Adaptation speed is crucial (user profile = interest) is crucial for filtering accuracy Interest changes: (profile ? interest) = filtering quality drops Adapt profile as fast as possible Subject of this thesis:Filtering architecture for maximum adaptation speed Introduction Requirements and related work The TV Scout …as a retrieval system …and as a filtering system How it works The QuerySet Architecture Building QuerySet filtering systems Manual profile editing Conclusions Requirements Requirement 1: Exhaustiveness (arbitrary interests) (King and Sacramento), but not (King and Queen), INFOS [Mock 96] Requirement 2: Output style (single ranking preferred) Boolean output, Info. Lens [Malone 87]; Categories, SIFT [Yan 95] Requirements 3-5: Adapt to interest changes R3: Learning from relevance feedback R4: Limitations of manual profile editing Resulting design guideline Build a filtering system that allows learning from relevance feed