Matrix Factorization Techniques for Recommender Systems

This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Regression Analysis of Count Data: Preface

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

http://www.cs.technion.ac.il/%7Ercohen/PAPERS/GAP.pdf

An efficient approximation for the generalized assignment problem

A prominent approach in collaborative filtering based recommender systems is using dimensionality reduction (matrix factorization) techniques to map users and items into low-dimensional vectors. In such systems, a higher inner product between a user vector and an item vector indicates that the item better suits the user's preference. Traditionally, retrieving the most suitable items is done by scoring and sorting all items. Real world online recommender systems must adhere to strict response-time constraints, so when the number of items is large, scoring all items is intractable.We propose a novel order preserving transformation, mapping the maximum inner product search problem to Euclidean space nearest neighbor search problem. Utilizing this transformation, we study the efficiency of several (approximate) nearest neighbor data structures. Our final solution is based on a novel use of the PCA-Tree data structure in which results are augmented using paths one hamming distance away from the query (neighborhood boosting). The end result is a system which allows approximate matches (items with relatively high inner product, but not necessarily the highest one). We evaluate our techniques on two large-scale recommendation datasets, Xbox Movies and Yahoo~Music, and show that this technique allows trading off a slight degradation in the recommendation quality for a significant improvement in the retrieval time.

/pdf/speeding-up-the-xbox-recommender-system-using-a-euclidean-3is1s1n9wu.pdf

Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces

A system and method for assigning one or more tags to an image file. In one aspect, a server computer receives an image file captured by a client device. In one embodiment, the image file includes an audio component embedded therein by the client device, where the audio component was spoken by a user of the client device as a tag of the image file. The server computer determines metadata associated with the image file and identifies a dictionary of potential textual tags from the metadata. The server computer determines a textual tag from the audio component and from the dictionary of potential textual tags. The server computer then associates the textual tag with the image file as additional metadata.

Automatic tag extraction from audio annotated photos

Online social networks have become very popular in recent years and their number of users is already measured in many hundreds of millions. For various commercial and sociological purposes, an independent estimate of their sizes is important. In this work, algorithms for estimating the number of users in such networks are considered. The proposed schemes are also applicable for estimating the sizes of networks' sub-populations. The suggested algorithms interact with the social networks via their public APIs only, and rely on no other external information. Due to obvious traffic and privacy concerns, the number of such interactions is severely limited. We therefore focus on minimizing the number of API interactions needed for producing good size estimates. We adopt the abstraction of social networks as undirected graphs and use random node sampling. By counting the number of collisions or non-unique nodes in the sample, we produce a size estimate. Then, we show analytically that the estimate error vanishes with high probability for smaller number of samples than those required by prior-art algorithms. Moreover, although our algorithms are provably correct for any graph, they excel when applied to social network-like graphs. The proposed algorithms were evaluated on synthetic as well real social networks such as Facebook, IMDB, and DBLP. Our experiments corroborated the theoretical results, and demonstrated the effectiveness of the algorithms.

/pdf/estimating-sizes-of-social-networks-via-biased-sampling-xfjf75f3hg.pdf

Estimating sizes of social networks via biased sampling

http://www.cs.technion.ac.il/~lirank/pubs/2008-IPL-The-Generalized-Maximum-Coverage-Problem.pdf

Liran Katzir

Papers

An efficient approximation for the generalized assignment problem

Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces

Automatic tag extraction from audio annotated photos

Estimating sizes of social networks via biased sampling

The Generalized Maximum Coverage Problem