Lecture

Probabilistic Retrieval Models

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In course

CS-423: Distributed information systems

This course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems. ...

Description

This lecture discusses the Vector Space Retrieval Model, evaluating information retrieval, recall and precision tradeoff, F-Measure, accuracy metrics, precision/recall in ranked retrieval, and mean average precision. It also covers query likelihood models, language modeling, learning the model, and issues with Maximum Likelihood Estimation. The lecture further explores query expansion, user relevance feedback, the Rocchio Algorithm, and SMART algorithm for practical relevance feedback. Practical considerations and challenges in relevance feedback methods are also addressed.

Instructor

Karl Aberer

Karl Aberer received his PhD in mathematics in 1991 from the ETH Zürich. From 1991 to 1992 he was postdoctoral fellow at the International Computer Science Institute (ICSI) at the University of California, Berkeley. In 1992, he joined the Integrated Publication and Information Systems institute (IPSI) of GMD in Germany, where he was leading the research division Open Adaptive Information Management Systems. In 2000 he joined EPFL as full professor. Since 2005 he is the director of the Swiss National Research Center for Mobile Information and Communication Systems ( NCCR-MICS, www.mics.ch ). He is member of the editorial boards of VLDB Journal, ACM Transaction on Autonomous and Adaptive Systems and World Wide Web Journal. He has been consulting for the Swiss government in research and science policy as a member of the Swiss Research and Technology Council ( SWTR ) from 2003 - 2011.

Official source

Related concepts (30)

Modeling language

A modeling language is any artificial language that can be used to express data, information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure Programing language. A modeling language can be graphical or textual. Graphical modeling languages use a diagram technique with named symbols that represent concepts and lines that connect the symbols and represent relationships and various other graphical notation to represent constraints.

Unified Modeling Language

The unified modeling language (UML) is a general-purpose visual modeling language that is intended to provide a standard way to visualize the design of a system. UML provides a standard notation for many types of diagrams which can be roughly divided into 3 main groups: behavior diagrams, interaction diagrams, and structure diagrams. The creation of UML was originally motivated by the desire to standardize the disparate notational systems and approaches to software design.

Systems modeling language

The systems modeling language (SysML) is a general-purpose modeling language for systems engineering applications. It supports the specification, analysis, design, verification and validation of a broad range of systems and systems-of-systems. SysML was originally developed by an open source specification project, and includes an open source license for distribution and use. SysML is defined as an extension of a subset of the Unified Modeling Language (UML) using . The language's extensions were designed to support systems engineering activities.

Object-modeling language

An object-modeling language is a standardized set of symbols used to model a software system using an object-oriented framework. The symbols can be either informal or formal ranging from predefined graphical templates to formal object models defined by grammars and specifications. A modeling language is usually associated with a methodology for object-oriented development. The modeling language defines the elements of the model. E.g., that a model has classes, methods, object properties, etc.

Domain-specific modeling

Domain-specific modeling (DSM) is a software engineering methodology for designing and developing systems, such as computer software. It involves systematic use of a domain-specific language to represent the various facets of a system. Domain-specific modeling languages tend to support higher-level abstractions than general-purpose modeling languages, so they require less effort and fewer low-level details to specify a given system.

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Probabilistic Information Retrieval

Covers probabilistic information retrieval, query likelihood models, language modeling, and relevance feedback algorithms.

Probabilistic Retrieval: Practical Relevance Feedback

Explores practical relevance feedback in probabilistic retrieval and query optimization.

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Introduces information retrieval basics, emphasizing document frequency and precision in evaluating retrieval quality.

Estimators and Confidence Intervals

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Information Retrieval Basics