PPT-Inferring User Interest Familiarity and Topic Similarity wi

Department of Computer Science KAIST Dabi Ahn Taehun Kim Soon J Hyun Dongman Lee IEEE 2012 Web Intelligence and Intelligent Agent Technology System Workflow Inferring User Interest Using Topic Structure. MORELAND University of Pittsburgh AND ROBERT B ZAJONC University of Michigan Received December 3 1980 Two experiments explored the relationship between familiarity similarity and attraction In the first experiment subjects viewed photographs of face David Kauchak. cs160. Fall . 2009. Administrative. Hw5/paper review. what . would be useful for the . authors. technical. , refers to not only the results, but the rest of the . paper. give . as many specific examples as . Robert Dyer. Bowling Green. State University. Tien. N. Nguyen. Iowa State . University. . Hridesh Rajan. Iowa State. University. Gary T. Leavens. University of. Central Florida. 2. Lack of . specifications* . Instructor: . Dongchul. . kim. Anusha boothpur. 20303325.  . INTRODUCTION. A. ctive . users converse with their . social neighbors . via social activities such as posting comments . one after . another. Normally, users perform search tasks using multiple applications in concert: a search engine interface presents lists of potentially relevant documents; a document reader displays their contents; and a third tool—a text editor or personal information management application—is used to record notes and assessments (MS Word and MS PowerPoint). . THOSE … THIS … THAT. 1. what . you’ll read next summer . (Amazon, . Barnes&Noble. ). . what movies you should watch… . (Reel, . RatingZone. , Amazon. ). what websites you should visit . (Alexa). David Kauchak. cs458. Fall 2012. Administrative. Schedule. Readings. Lunch today!. HW4 due tomorrow. Attendance. Today’s class. Blend of introductory material and research talk. Problem of topic segmentation. Ahmet Murat UZUN, . Ph.D. .. Rationale. Although various factors may be involved, one reason leading to low motivation is lack of interest. .. Interest was defined as “the psychological state of engaging or the predisposition to reengage with particular classes of objects, events, or ideas over time” (. Acknowledgements : This research is supported by NSF grant 0938074 INTRODUCTION MULTI LAYER PERCEPTRONS (MLP) DATA SET FOR TRAINING Learning weights using multi-layer perceptron in User Interest Modeling Text Similarity. Motivation. People can express the same concept (or related concepts) in many different ways. For example, “the plane leaves at 12pm” vs “the flight departs at noon”. Text similarity is a key component of Natural Language Processing. Quiz. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. 3. Deer-mouse. 4. Deer-roof. Quiz Answer. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. Sampath Jayarathna. Cal Poly Pomona. Hierarchical Clustering. Build a tree-based hierarchical taxonomy (. dendrogram. ) from a set of documents.. One approach: recursive application of a . partitional. Basri Kahveci, Burak Kocuroğlu, Christina Kirchner. 1. / 17. Outline. Introduction. Methodology. Dataset. Experiments & Results. Future Work. Questions. 2. Introduction. We . tend . to like things that are similar to other things . Sketching, Locality Sensitive Hashing. SIMILARITY AND DISTANCE. Thanks to:. Tan, Steinbach, . and Kumar, “Introduction to Data Mining”. Rajaraman. . and . Ullman, “Mining Massive Datasets”. Similarity and Distance.