Stanford University

About: Stanford University is a education organization based out in Stanford, California, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 125751 authors who have published 320347 publications receiving 21892059 citations. The organization is also known as: Leland Stanford Junior University & University of Stanford.

Fab: content-based, collaborative recommendation

Abstract: The problem of recommending items from some fixed database has been studied extensively, and two main paradigms have emerged. In content-based recommendation one tries to recommend items similar to those a given user has liked in the past, whereas in collaborative recommendation one identifies users whose tastes are similar to those of the given user and recommends items they have liked. Our approach in Fab has been to combine these two methods. Here, we explain how a hybrid system can incorporate the advantages of both methods while inheriting the disadvantages of neither. In addition to what one might call the “generic advantages” inherent in any hybrid system, the particular design of the Fab architecture brings two additional benefits. First, two scaling problems common to all Web services are addressed—an increasing number of users and an increasing number of documents. Second, the system automatically identifies emergent communities of interest in the user population, enabling enhanced group awareness and communications. Here we describe the two approaches for contentbased and collaborative recommendation, explain how a hybrid system can be created, and then describe Fab, an implementation of such a system. For more details on both the implemented architecture and the experimental design the reader is referred to [1]. The content-based approach to recommendation has its roots in the information retrieval (IR) community, and employs many of the same techniques. Text documents are recommended based on a comparison between their content and a user profile. Data

Optimally sparse representation in general (nonorthogonal) dictionaries via 1 minimization

Abstract: Given a dictionary D = {dk} of vectors dk, we seek to represent a signal S as a linear combination S = ∑k γ(k)dk, with scalar coefficients γ(k). In particular, we aim for the sparsest representation possible. In general, this requires a combinatorial optimization process. Previous work considered the special case where D is an overcomplete system consisting of exactly two orthobases and has shown that, under a condition of mutual incoherence of the two bases, and assuming that S has a sufficiently sparse representation, this representation is unique and can be found by solving a convex optimization problem: specifically, minimizing the l1 norm of the coefficients γ. In this article, we obtain parallel results in a more general setting, where the dictionary D can arise from two or several bases, frames, or even less structured systems. We sketch three applications: separating linear features from planar ones in 3D data, noncooperative multiuser encoding, and identification of over-complete independent component models.

A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation

Abstract: This is an invited expository article for The American Statistician. It reviews the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule. The presentation is written at a relaxed mathematical level, omitting most proofs, regularity conditions, and technical details.

First‐mover advantages

Abstract: The use of a fairly standard vehicle width of a little under 2 meters originates from the design of prehistoric carts and sleds as evidenced by rutting in ancient roads which aided in steering Despite dramatic advances in vehicular and infrastructural technologies, the standard has changed little over the millennia The gauges of railroad track, for instance, are now standardized at 4 feet 8 and half inches (1435 mm) across Europe and North America, the same as the first steam railway, and a mere half-inch wider than the typical pre-steam tracks in the mining districts near Newcastle, consistent in size with the wheel gauge used in Roman Britain This standard gauge lasted since it was first used on the Stockton and Darlington railway in 1825, and were adopted by most subsequent lines (Puffert, 2002), despite some railways trying alternatives (eg the Great Western Railway was originally built at 5 feet 6 inches, or 1676 mm) Alternative gauges would have accommodated wider, taller, and faster trains more easily, but the standard gauge that was adopted first acquired advantages as other railways sought compatibility with the standard to obtain access to the uses of earlier lines, and helped lock-in that standard

{SNAP Datasets}: {Stanford} Large Network Dataset Collection

Abstract: A collection of more than 50 large network datasets from tens of thousands of nodes and edges to tens of millions of nodes and edges. In includes social networks, web graphs, road networks, internet networks, citation networks, collaboration networks, and communication networks.