PDF-(BOOS)-Measuring User Engagement (Synthesis Lectures on Information Concepts Retrieval

User engagement refers to the quality of the user experience that emphasizes the positive aspects of interacting with an online application and in particular the desire to use that application longer and repeatedly User engagement is a key concept in the design of online applications whether for desktop tablet or mobile motivated by the observation that successful applications are not just used but are engaged with Users invest time attention and emotion in their use of technology and seek to satisfy pragmatic and hedonic needs Measurement is critical for evaluating whether online applications are able to successfully engage users and may inform the design of and use of applications User engagement is a multifaceted complex phenomenon this gives rise to a number of potential measurement approaches Common ways to evaluate user engagement include using selfreport measures eg questionnaires observational methods eg facial expression analysis speech analysis neurophysiological signal processing methods eg respiratory and cardiovascular accelerations and decelerations muscle spasms and web analytics eg number of site visits click depth These methods represent various tradeoffs in terms of the setting laboratory versus in the wild object of measurement user behaviour affect or cognition and scale of data collected For instance smallscale user studies are deep and rich but limited in terms of generalizability whereas largescale web analytic studies are powerful but negate users motivation and context The focus of this book is how user engagement is currently being measured and various considerations for its measurement Our goal is to leave readers with an appreciation of the various ways in which to measure user engagement and their associated strengths and weaknesses We emphasize the multifaceted nature of user engagement and the unique contextual constraints that come to bear upon attempts to measure engagement in different settings and across different user groups and web domains At the same time this book advocates for the development of good measures and good measurement practices that will advance the study of user engagement and improve our understanding of this construct which has become so vital in our wired worldTable of Contents Preface Acknowledgments Introduction and Scope Approaches Based on SelfReport Methods Approaches Based on Physiological Measurements Approaches Based on Web Analytics Beyond Desktop Single Site and Single Task Enhancing the Rigor of User Engagement Methods and Measures Conclusions and Future Research Directions Bibliography Authors Biographies Index. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Retrieval Models. Model is an idealization or abstraction of an actual process. in this case, process is matching of documents with queries, i.e., retrieval. Pattern Completion and Recapitulation. Episodic Retrieval and the Frontal Lobes. Cues for Retrieval. The Second Time Around: Recognizing Stimuli by Recollection and Familiarity. Misremembering the Past. Hui Fang , Tao . Tao. , . ChengXiang. . Zhai. University of Illinois at Urbana Champaign. SIGIR 2004 Best Paper. Presented by Lingjie Zhang. Outline. Formal Definitions of Heuristic Retrieval Constraints. By . Rong. Yan, Alexander G. and . Rong. Jin. Mwangi. S. . Kariuki. 2008-11629. Quiz. What’s Negative Pseudo-Relevance feedback in multimedia retrieval?. Introduction. As a result of high demand of content based access to video information.. INST 734. Module 3. Doug . Oard. Agenda. Ranked retrieval. Similarity-based ranking. Probability-based ranking. Boolean Retrieval. Strong points. Accurate, . if you know the right strategies. Efficient for the computer. CSC . 575. Intelligent Information Retrieval. 2. Source: . Intel. How much information?. Google: . ~100 . PB a . day; 3+ million servers (15 . Exabytes. stored). Wayback Machine has . ~9 . PB + . 100 . (for MODIS). Andy Harris. Jonathan . Mittaz. Prabhat. . Koner. (Chris Merchant, Pierre . LeBorgne. ). Satellite data – pros and cons. Main advantages of satellite data. Frequent and regular global coverage (cloud cover permitting for IR). Information Retrieval. Information Retrieval. Konsep. . dasar. . dari. IR . adalah. . pengukuran. . kesamaan. sebuah. . perbandingan. . antara. . dua. . dokumen. , . mengukur. . sebearapa. . Wang. CS@UVa. Explicit relevance feedback. 2. Updated. query. Feedback. Judgments:. d. 1 . . d. 2. -. d. 3 . …. d. k. -. .... Query. User . judgment. Retrieval. Engine. Document. collection. Results:. . Parts 1-4 – Data Extraction, Quality Assessment, Synthesising Across Studies, . Completing the Analysis. Workshop: . Framework Synthesis, Meta-Ethnography and Realist Synthesis . Shared Topic: . The Next Frontier in Video Metrics . David Parker Peter Ciuffetti. VP Video Distribution VP Product Development. Agenda. User Engagement in the Video Space. How and What We Can Measure. How this Leads to Insight on Impact. What is IR?. Sit down before fact as a little child, . be prepared to give up every conceived notion, . follow humbly wherever and whatever abysses nature leads, . or you will learn nothing. . . -- Thomas Huxley --. Do . Your . Students . Look Like This?. Does Your . Powerpoint. Look Like This?. Teaching Tips and Tricks. Karen Jansen. Make it Engaging. Harvard. BBC. King’s College. Logos and Pathos. “The . best presentations are good . Tegwen Green. GSS Good Practice Team. “The UK Statistics Authority should develop a strategy for engaging all users of official statistics”. In our response we committed to… . “ . developing an engagement strategy and implementation plan with Heads of Profession (.