Georg Buscher German Research Center for Artificial Intelligence (DFKI) Knowledge Management Department Kaiserslautern, Germany SIGIR 07 Doctoral Consortium Attention-Based Information Retrieval

Georg Buscher Motivation  Magnetic Resonance Imaging uses magnetic fields and radio waves to produce high quality two- or three-dimensional images of brain structures. Sensors read frequencies of radio waves and a computer uses the information to construct an image of the brain (see 2).  Positron Emission Tomography measures emissions from radioactively labeled metabolically active chemicals that have been injected into the bloodstream. The emission data are computer-processed to produce 2- or 3-dimensional images of the distribution of the chemicals throughout the brain. Especially useful are a wide array of chemicals used to map different aspects of neurotransmitter activity (see 3).  Homer's personality is one of frequent stupidity, laziness, and explosive anger. He also suffers from a short attention span which complements his intense but short- lived passion for hobbies, enterprises and various causes. Furthermore, he is prone to emotional outbursts. 123

Georg Buscher Outline  Acquiring attention evidence –Attention evidence through eye tracking –Attention annotation and derivation with Dempster-Shafer  Applications in Information Retrieval –Attention-based TfIdf –Context elicitation –Context-based Index –Query Expansion / result re-ranking

Georg Buscher Sources of Attention-Data  There are many indications of attention from the user: read skimmed longer viewed Annotations (explicit) Reading evidence (implicit)

Georg Buscher Reading Detection – An Example

Georg Buscher Attention Annotations Imply Different Levels of Attention  Attention evidence values [0.7; 1.0][0.5; 1.0] [0.2; 0.7] [1.0; 1.0] … ……  Range from 0 to 1  Width of an interval expresses uncertainty

Georg Buscher Dempster-Shafer Combination of Attention Evidence read [The demo … provide][different][visualizations][and interfaces][according … situation.] RR HR H U R U R [0.5; 1][0.85; 1][0.96; 1] [0.85; 1] [0.5; 1] Calculate one value of attention (att(t) = bel(t) – 0.2*bel(t) + 0.2*pl(t)) : In that way, the function att provides an attention value for every term of the document. att different, d = 0.88 att according, d = 0.6 att somethingElse, d = 0

Georg Buscher Outline  Acquiring attention evidence –Attention evidence through eye tracking –Attention annotation and derivation with Dempster-Shafer  Applications in Information Retrieval –Attention-based TfIdf Desktop Index –Context elicitation –Context-based Index –Query Expansion

Georg Buscher Attention-Based Desktop Index  A Desktop index is especially for re-finding known documents.  You can better remember those parts of a document that you paid attention to.   Attended terms should be weighted higher.  TfIdf-based modification –Attention is a local factor (like tf) –The higher the maximal intensity of an attended document part, the more weight should be assigned to the attention value. –The lower the maximal intensity of an attended document part, the more weight should be assigned to tf. attention partterm frequency part tf t,d : term frequency of term t in document d att t,d : attention value of term t in document d α in [0; 1] is a balancing factor for defining the influence of attention in contrast to term frequency.

Georg Buscher Why Context? The Search for the Mental Model  If a knowledge worker tries to recall something concerning a topic, does he primarily think –on the basis of documents and document structures or –on the basis of former thematic contexts?  Rather the latter…  While re-finding some information, one does not search primarily for the document, but for the former mental model. Documents mediate.

Georg Buscher Elicitation and Representation of the Thematic Context Document 1 Brain imaging Document 2 Brain imaging Document 3 The Simpsons thematic context Brain imaging  Some read sub- documents  Combination of the viewed sub- documents to one virtual context document (only those attended parts that have a thematic overlapping) Document 4 Brain imaging

Georg Buscher Determination of Thematical Overlapping  Determine buzzwords for each viewed document by using –Attention value –Idf of desktop index  Compare buzzword vector with previous context vectors –If there is a similarity, then merge with context vector –Else buzzword vector is a new context ? Previous contexts Currently viewed document (part)

Georg Buscher  Idea: two indexes 1. Term – Context2. Context – Document  A context is represented by a virtual context document  The value for each term–context relation is influenced by the degree of attention Context-Based Vector-Space Index  Common index structure Doc1 Doc2 Doc3 Term1 Term2 Term C1 C2 C3 Term1 Term2 Term3 Term Doc1 Doc2 C1 C2 C3 xxxx xxxx

Georg Buscher New Kinds of Search Tasks Possible  Local search: Find for the current task (parts of) documents, that I formerly used for a similar task.  Enterprise-wide search: Find for the current task (parts of) documents, that I do not know yet, but that have been used by some colleague for a similar task.

Georg Buscher Evaluation of the Context-Based Index  Main advantage is expected to show up in several weeks.  Not possible to do real-world eye tracking studies for such a long time  Artificial experiment: –Several different exploration tasks within some hours –Then some re-finding tasks about previously viewed content –Measuring the time or user- satisfaction during the search process? Context-based search Normal search

Georg Buscher Contextual Attention-Based Relevance Feedback  Problem with context-based index: it doesn’t scale for web search  therefore query expansion  Current elicited context (i.e. term vector) expresses current interest of the user  Topmost characteristic keywords will be used for query expansion

Georg Buscher Attention data generation module Eye Tracker Text Mark Recognition Attention-annotated document The Global Picture Attention-based desktop index Context-based index Context document Query expansion for web search attention Thank you for your !attention