PDF-Explicit Factor Models for Explainable Recommendation

comzmyiqunliumsptsinghuaeducnyizsoeucscedu ABSTRACT Collaborative FilteringCFbased recommendation algorithms such as Latent Factor Models LFM work well in terms of prediction accuracy However the latent features make it di64259culty to explain the re. Martin . Ja. čala and . Jozef Tvarožek. Špindlerův. . Mlýn. , Czech Republic. January 23, 2012. Slovak University of Technology. Bratislava, Slovakia. Problem. Given an input text, detect and decide on correct meaning of named entites. Presenters:. Kim Wilson, Career Center. Natalia Dyba, Merit Scholarships. Wednesday, October 30, 3:30-5:00pm. UW1-103. UW BOTHELL OFFICE OF MERIT SCHOLARSHIPS, FELLOWSHIPS AND AWARDS AND THE CAREER CENTER . Whom to ask for reference and letters of recommendation?. They must:. Know you well (e.g., taken for multiple classes, done a directed study with, talk outside of class). Have known you for a prolonged period of time. First Unitarian Universalist Church of . Richmond. Living the Pledge . Workshop. How does implicit and explicit bias differ?. Attitudes . or stereotypes that affect our understanding, actions, and decisions in . Abstract. Recent years have witnessed an increased interest in recommender systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. Criteo. Simon . Dollé. RecSys. . FR, . December. 1. st. , . 2015. We buy. Ad spaces. We buy. Ad spaces. We sell. Clicks. We buy. Ad spaces. We sell. Clicks. that convert. We buy. Ad spaces. We sell. Danielle Lee . April 20, 2011. Three basic recommendations . Collaborative Filtering. : exploiting other likely-minded community data to derive recommendations. Effective, Novel and Serendipitous recommendations . ManetS. Adeela Huma. 02/02/2017. Introduction - MANETs. MANETs- Mobile ad hoc networks . lacks infrastructure and . central . authority to . establish and . facilitate communication . in the . network. Committee of Ministers Recommendation. World’s first comprehensive inter-governmental agreement on LGBT rights. Extensive inclusion of . g.i. . issues. Sets out . measures. which states should take to ensure that . Prepared by Tahani Alahmadi. Objectives. After completing this lecture, you should be able to do. the following:. • . Distinguish between an implicit and an explicit cursor. • . Discuss when and why to use an explicit cursor. Phillip . Wood, Wolfgang . Wiedermann. , . Douglas . Steinley. University of Missouri. Some Questions We Wish We Could Answer with Longitudinal Data. Are there Different Types of Learners? . Slow Versus Quick. Jeff Harrison, MD. Department of Family Medicine. Faculty Development. Really????. How hard can it be to write a letter of recommendation. After all….. . we. are highly educated . You would be surprised at the frequency of unhelpful letters we see. By Catherine Kelley. 2 common dichotomies in grammar instruction:. 1. explicit vs. implicit. 2. . deductive vs. inductive. Explicit vs. implicit pertains to whether or not rules are provided . Explicit grammar instruction involves explanation of rules and metalinguistic feedback. . Avoiding Bias in interviewing Storytellers. By Michael Preston Ed.D.. What is Bias?. Bias is prejudice against a person or group of people when compared to others. These biases are usually based on prior attitudes, first impressions, or socially constructed stereotypes. .