萨义德以其独特的双重身份,对西方中心权力话语做了分析,通过对文学作品、演讲演说等文本的解读,将O rie n ta lis m——"东方学",做了三重释义:一门学科、一种思维方式和一种权力话语系统,对东方学权力话语做了系统的批判,同时将东方学放入空间维度对东方学文本做了细致的解读。

萨义德的“Orientalism”释义

The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.

The MovieLens Datasets: History and Context

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.

/pdf/content-based-recommendation-systems-2zvilwe3wz.pdf

Content-based recommendation systems

We present a new class of statistical de- anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information

/pdf/robust-de-anonymization-of-large-sparse-datasets-4c1lqr3b3t.pdf

Robust De-anonymization of Large Sparse Datasets

In support of social interaction and information sharing, online communities commonly provide interfaces for users to form or interact with groups. For example, a user of the social music recommendation site last.fm might join the "First Wave Punk" group to discuss his or her favorite band (The Clash) and listen to playlists generated by fellow fans. Clustering techniques provide the potential to automatically discover groups of users who appear to share interests. We explore this idea by describing algorithms for clustering users of an online community and automatically describing the resulting user groups. We designed these techniques for use in an online recommendation system with no pre-existing group functionality, which led us to develop an "activity-balanced clustering" algorithm that considers both user activity and user interests in forming clusters.

/pdf/supporting-social-recommendations-with-activity-balanced-21n8jo7iut.pdf

Supporting social recommendations with activity-balanced clustering

In addition to encyclopedia articles and software, peer production communities produce structured data, e.g., Wikidata and OpenStreetMap's metadata. Structured data from peer production communities has become increasingly important due to its use by computational applications, such as CartoCSS, MapBox, and Wikipedia infoboxes. However, this structured data is usable by applications only if it follows standards. We did an interview study focused on OpenStreetMap's knowledge production processes to investigate how -- and how successfully -- this community creates and applies its data standards. Our study revealed a fundamental tension between the need to produce structured data in a standardized way and OpenStreetMap's tradition of contributor freedom. We extracted six themes that manifested this tension and three overarching concepts, correctness, community, and code, which help make sense of and synthesize the themes. We also offered suggestions for improving OpenStreetMap's knowledge production processes, including new data models, sociotechnical tools, and community practices (e.g. stronger leadership).

Freedom versus Standardization: Structured Data Generation in a Peer Production Community

People from different cultures vary in cognition, emotion, and behavior. We explore cultural differences in a tagging system. We developed a model of cultural differences and performed a controlled empirical study with American and Chinese subjects to investigate questions that arise from the model. American and Chinese subjects differed in many ways: the number and types of tags they applied; the extent to which they applied suggested tags or entered new tags of their own; and how often they applied tags that originated from a different culture. Our results are consistent with theories of cultural differences between Asian and Western cultures. Our findings suggest new opportunities and mechanisms for shaping user behavior to produce useful tag repositories.

https://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/download/4624/4970

War Versus Inspirational in Forrest Gump: Cultural Effects in Tagging Communities

In the United States, the public has a constitutional right to access criminal trial proceedings. In practice, it can be difficult or impossible for the public to exercise this right. We present JUSTFAIR: Judicial System Transparency through Federal Archive Inferred Records, a database of criminal sentencing decisions made in federal district courts. We have compiled this data set from public sources including the United States Sentencing Commission, the Federal Judicial Center, the Public Access to Court Electronic Records system, and Wikipedia. With nearly 600,000 records from the years 2001-2018, JUSTFAIR is the first large scale, free, public database that links information about defendants and their demographic characteristics with information about their federal crimes, their sentences, and, crucially, the identity of the sentencing judge.

/pdf/justfair-judicial-system-transparency-through-federal-3qv7ox7xut.pdf

JUSTFAIR: Judicial System Transparency through Federal Archive Inferred Records

Embodiments of the present invention address deficiencies of the art in respect to alert bubble management and provide a method, data processing system and computer program product for interactive alert bubbles for alert bubble management. In one embodiment of the invention, an interactive alert bubble can be provided for an alert management data processing system. The interactive alert bubble can include an alert bubble interface to the interactive alert bubble. An alert text field can be disposed in the interface and configured to provide a reference to an alert notification in the alert management data processing system. Additionally, a rating control can be disposed in the interface and configured to establish a rating for the alert notification responsive to a selection of the rating control.

Shilad Sen

Papers

Supporting social recommendations with activity-balanced clustering

Freedom versus Standardization: Structured Data Generation in a Peer Production Community

War Versus Inspirational in Forrest Gump: Cultural Effects in Tagging Communities

JUSTFAIR: Judicial System Transparency through Federal Archive Inferred Records

Interactive alert bubbles for alert management