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

This paper presents WiSee, a novel gesture recognition system that leverages wireless signals (e.g., Wi-Fi) to enable whole-home sensing and recognition of human gestures. Since wireless signals do not require line-of-sight and can traverse through walls, WiSee can enable whole-home gesture recognition using few wireless sources. Further, it achieves this goal without requiring instrumentation of the human body with sensing devices. We implement a proof-of-concept prototype of WiSee using USRP-N210s and evaluate it in both an office environment and a two- bedroom apartment. Our results show that WiSee can identify and classify a set of nine gestures with an average accuracy of 94%.

/pdf/whole-home-gesture-recognition-using-wireless-signals-4tosup7gjr.pdf

Whole-home gesture recognition using wireless signals

The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics.Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5541&context=sis_research

From RSSI to CSI: Indoor localization via channel response

The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Modern communication technologies are steadily advancing the physical layer (PHY) data rate in wireless LANs, from hundreds of Mbps in current 802.11n to over Gbps in the near future. As PHY data rates increase, however, the overhead of media access control (MAC) progressively degrades data throughput efficiency. This trend reflects a fundamental aspect of the current MAC protocol, which allocates the channel as a single resource at a time.This paper argues that, in a high data rate WLAN, the channel should be divided into separate subchannels whose width is commensurate with PHY data rate and typical frame size. Multiple stations can then contend for and use subchannels simultaneously according to their traffic demands, thereby increasing overall efficiency. We introduce FICA, a fine-grained channel access method that embodies this approach to media access using two novel techniques. First, it proposes a new PHY architecture based on OFDM that retains orthogonality among subchannels while relying solely on the coordination mechanisms in existing WLAN, carrier-sensing and broadcasting. Second, FICA employs a frequency-domain contention method that uses physical layer RTS/CTS signaling and frequency domain backoff to efficiently coordinate subchannel access. We have implemented FICA, both MAC and PHY layers, using a software radio platform, and our experiments demonstrate the feasibility of the FICA design. Further, our simulation results suggest FICA can improve the efficiency ratio of WLANs by up to 400% compared to existing 802.11.

/pdf/fine-grained-channel-access-in-wireless-lan-44ajxduwv9.pdf

Fine-grained channel access in wireless LAN

We study the combinatorial pure exploration (CPE) problem in the stochastic multi-armed bandit setting, where a learner explores a set of arms with the objective of identifying the optimal member of a decision class, which is a collection of subsets of arms with certain combinatorial structures such as size-K subsets, matchings, spanning trees or paths, etc. The CPE problem represents a rich class of pure exploration tasks which covers not only many existing models but also novel cases where the object of interest has a non-trivial combinatorial structure. In this paper, we provide a series of results for the general CPE problem. We present general learning algorithms which work for all decision classes that admit offline maximization oracles in both fixed confidence and fixed budget settings. We prove problem-dependent upper bounds of our algorithms. Our analysis exploits the combinatorial structures of the decision classes and introduces a new analytic tool. We also establish a general problem-dependent lower bound for the CPE problem. Our results show that the proposed algorithms achieve the optimal sample complexity (within logarithmic factors) for many decision classes. In addition, applying our results back to the problems of top-K arms identification and multiple bandit best arms identification, we recover the best available upper bounds up to constant factors and partially resolve a conjecture on the lower bounds.

/pdf/combinatorial-pure-exploration-of-multi-armed-bandits-1x0t9444z5.pdf

Combinatorial Pure Exploration of Multi-Armed Bandits

Recommender systems are faced with new challenges that are beyond traditional techniques. For example, most traditional techniques are based on similarity or overlap among existing data, however, there may not exist sufficient historical records for some new users to predict their preference, or users can hold diverse interest, but the similarity based methods may probably over-narrow it. To address the above challenges, we develop a principled approach called contextual combinatorial bandit in which a learning algorithm can dynamically identify diverse items that interest a new user. Specifically, each item is represented as a feature vector, and each user is represented as an unknown preference vector. On each of n rounds, the bandit algorithm sequentially selects a set of items according to the item-selection strategy that balances exploration and exploitation, and collects the user feedback on these selected items. A reward function is further designed to measure the quality (e.g. relevance or diversity) of the selected set based on observed feedback, and the goal of the algorithm is to maximize the total rewards of n rounds. The reward function only needs to satisfy two mild assumptions that is general enough to accommodate a large class of nonlinear functions. To solve this bandit problem, we provide algorithm that achieves Õ( √ n) regret after playing n rounds. Experiments conducted on real-wold movie recommendation dataset demonstrate that our approach can effectively address the above challenges and hence improve the performance of recommendation task. †State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology. Dept. of Computer Science and Technology, Tsinghua University, Beijing, China. Emails: qinlj09@mail.tsinghua.edu.cn, zxy-dcs@tsinghua.edu.cn. ‡Dept. of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. Email: sychen@cse.cuhk.edu.hk.

Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation.

With the increasing of physical-layer (PHY) data rate in modern wireless local area networks (WLANs) (e.g., 802.11n), the overhead of media access control (MAC) progressively degrades data throughput efficiency. This trend reflects a fundamental aspect of the current MAC protocol, which allocates the channel as a single resource at a time. This paper argues that, in a high data rate WLAN, the channel should be divided into separate subchannels whose width is commensurate with the PHY data rate and typical frame size. Multiple stations can then contend for and use subchannels simultaneously according to their traffic demands, thereby increasing overall efficiency. We introduce FICA, a fine-grained channel access method that embodies this approach to media access using two novel techniques. First, it proposes a new PHY architecture based on orthogonal frequency division multiplexing (OFDM) that retains orthogonality among subchannels while relying solely on the coordination mechanisms in existing WLAN, carrier sensing and broadcasting. Second, FICA employs a frequency-domain contention method that uses physical-layer Request to Send/Clear to Send (RTS/CTS) signaling and frequency domain backoff to efficiently coordinate subchannel access. We have implemented FICA, both MAC and PHY layers, using a software radio platform, and our experiments demonstrate the feasibility of the FICA design. Furthermore, our simulation results show FICA can improve the efficiency of WLANs from a few percent to 600% compared to existing 802.11.

Ridge regression is one of the most popular and effective regularized regression methods, and one case of particular interest is that the number of features p is much larger than the number of samples n, i.e. p ≫ n. In this case, the standard optimization algorithm for ridge regression computes the optimal solution x* in O(n2p + n3) time. In this paper, we propose a fast relative-error approximation algorithm for ridge regression. More specifically, our algorithm outputs a solution x satisfying ‖x - x*‖2 ≤ e‖x*‖2 with high probability and runs in O(nnz(A)+ n3/e2) time, where nnz(A) is the number of non-zero entries of matrix A.

To the best of our knowledge, this is the first algorithm for ridge regression that runs in o(n2p) time with provable relative-error approximation bound on the output vector. In addition, we analyze the risk inflation bound of our algorithm and apply our techniques to two generalizations of ridge regression, including multiple response ridge regression and a non-linear ridge regression problem. Finally, we show empirical results on both synthetic and real datasets.

/pdf/fast-relative-error-approximation-algorithm-for-ridge-45wzt3nsr1.pdf

Shouyuan Chen

Papers

Fine-grained channel access in wireless LAN

Combinatorial Pure Exploration of Multi-Armed Bandits

Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation.

Fine-grained channel access in wireless LAN

Fast relative-error approximation algorithm for ridge regression