Talent search and recommendation systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter or a hiring manager expressed in terms of a search query or a job posting. Recent work in this domain has mainly focused on linear models, which do not take complex relationships between features into account, as well as ensemble tree models, which introduce non-linearity but are still insufficient for exploring all the potential feature interactions, and strictly separate feature generation from modeling. In this paper, we present the results of our application of deep and representation learning models on LinkedIn Recruiter. Our key contributions include: (i) Learning semantic representations of sparse entities within the talent search domain, such as recruiter ids, candidate ids, and skill entity ids, for which we utilize neural network models that take advantage of LinkedIn Economic Graph, and (ii) Deep models for learning recruiter engagement and candidate response in talent search applications. We also explore learning to rank approaches applied to deep models, and show the benefits for the talent search use case. Finally, we present offline and online evaluation results for LinkedIn talent search and recommendation systems, and discuss potential challenges along the path to a fully deep model architecture. The challenges and approaches discussed generalize to any multi-faceted search engine.

In this talk, we present the overall system design and architecture, the challenges encountered in practice, and the lessons learned from the production deployment of the talent search and recommendation systems at LinkedIn. By presenting our experiences of applying techniques at the intersection of recommender systems, information retrieval, machine learning, and statistical modeling in a large-scale industrial setting and highlighting the open problems, we hope to stimulate further research and collaborations within the SIGIR community.

One key challenge in talent search is to translate complex criteria of a hiring position into a search query, while it is relatively easy for a searcher to list examples of suitable candidates for a given position. To improve search e ciency, we propose the next generation of talent search at LinkedIn, also referred to as Search By Ideal Candidates. In this system, a searcher provides one or several ideal candidates as the input to hire for a given position. The system then generates a query based on the ideal candidates and uses it to retrieve and rank results. Shifting from the traditional Query-By-Keyword to this new Query-By-Example system poses a number of challenges: How to generate a query that best describes the candidates? When moving to a completely di erent paradigm, how does one leverage previous product logs to learn ranking models and/or evaluate the new system with no existing usage logs? Finally, given the di erent nature between the two search paradigms, the ranking features typically used for Query-By-Keyword systems might not be optimal for Query- By-Example. This paper describes our approach to solving these challenges. We present experimental results con rming the e ectiveness of the proposed solution, particularly on query building and search ranking tasks. As of writing this paper, the new system has been available to all LinkedIn members.

This paper presents a mathematical framework for the evaluation of the performance of proactive and reactive routing protocols in mobile ad hoc networks (MANETs). This unified framework provides a parametric view of protocol performance, which in turn provides a deeper insight into protocol operations and reveals the compounding and interacting effects of protocol logic and network parameters. The parametric model comes from a combinatorial model, where the routing logic is synthesized along with the characterization of MAC performance. Each wireless node is seen independently as a two-customer queue without priority, where the two types of customers are unicast and broadcast packets. The model captures the essential behavior and scalability limits in network size of both classes of routing protocols, and provides valuable guidance on the performance of reactive or proactive routing protocols under various network configurations and mobility conditions. The analytical results obtained with the proposed model are in close agreement with simulation results obtained from discreteevent Qualnet simulations.

We present statistical models to accurately evaluate the distribution of the lifetime of wireless links in a mobile ad hoc network (MANET) in which nodes move randomly within constrained areas. We show that link lifetime can be computed through a two-state Markov model and further apply the computed statistics to the optimization of segmentation schemes of an information stream. Summarizing all these results, we further provide a comprehensive analysis on throughput, delay, and storage requirements for MANETs with restricted node mobility.

We present a mathematical framework for the performance evaluation of proactive and reactive routing protocols operating in mobile ad hoc networks (MANETs). The model captures the functionality of the routing protocols together with the characterization of the performance of the medium access control protocol (MAC). It reveals the interplay between the protocol functionality and network parameters, and provides new insight on the relative benefits of proactive and on-demand routing in MANETS. The analytical results are corroborated with results obtained using discrete-event simulations.

rdquoThe paper presents a mathematical framework for quantifying the overhead of proactive routing protocols in mobile ad hoc networks (MANETs). We focus on situations where the nodes are randomly moving around but the wireless transmissions can be decoded reliablely, when nodes are within communication range of each other. We explicitly present a framework to model the overhead as a function of stability of topology and analytically characterize the statistical distribution of topology evolutions. The OLSR protocol is further singled out for a detailed analysis, incorporating the proposed analytical model. Results are compared against Qualnet simulations for random movements, which corroborate the essential characteristics of the analytical results. The key insight that can be drawn from the analytical results of this paper is that nodal movements will drive up the overhead by a penalty factor, which is a function of the overall stability of the network.

This paper considers the problem of decentralized data fusion (DDF) for large wireless sensor networks with stringent bandwidth requirements. To reduce the power and bandwidth costs of wireless transmissions, each sensor node is confined to quantize its sensing data and send 1-bit information only. Under this setting, we derive the maximum likelihood (ML) data fusion rule for decentralized parameter estimation, and analyze its Cramer-Rao lower bound (CRLB) of the fusion performance in the sense of mean square distortion. Depending on the underlying noise characteristics, our 1-bit DDF scheme can achieve estimation performance competitive to or even surprisingly better than that of centralized fusion over unquantized data. There is considerable saving in communication costs, which in turn reduces network energy consumption. Furthermore, we investigate network optimization, for which a worst-case robust design methodology is adopted to formulate a well-behaved min/max optimization problem. From the information processing viewpoint, the resulting optimized network offers robust fusion performance at minimal costs of communication resources.

Abstract — For pulsed ultra-wideband (UWB) radios, a major challenge in receiver design is to collect sufficient energy from ultra-short pulses exposing to strong multipath scattering. We develop a UWB receiver structure along with low-complexity timing synchronization and data demodulation schemes base on noisy templates (NT). The NT receiver design enables sufficient energy capture with full multipath diversity, and achieves asymptotically optimal detection performance with robustness to mis-timing. To alleviate the noise effect, a decision directed (DD) scheme is presented to lower the noise variance of the template. The detection error performance of the NT receiver is analyzed and compared with that of RAKE receivers, under realistic channel and timing estimation errors. Insights on the design tradeoffs of NT versus RAKE reception are provided, using unifying metrics that capture the relative importance of various performance-critical factors of individual receivers in the UWB regime. Both analysis and simulations confirm that the NT receiver outperforms the RAKE with a limited number of fingers under practical operating conditions. Index Terms — pulsed UWB, synchronization, detection and receiver structure, noisy template, RAKE, asymptotic optimality I.

Two popular UWB receivers, the RAKE correlator and noisy-template (NT) auto-correlator, are evaluated and compared under realistic channel and timing estimation errors. A unified performance analysis framework is developed under the notion of receiver operating efficiency (ROE), which measures the effective received energy of a practical receiver against that of an ideal receiver. ROE expressions show that a RAKE receiver is limited by its energy capture capability, which is reflected not only in the number of RAKE fingers employed, but also in the timing offset estimator errors occurring at these fingers. An NT receiver is fairly robust to timing errors but is subject to noise enhancement effects, which can be alleviated by using transmissions with higher duty cycle and more training symbols. Through ROE, these critical system parameters are evaluated in terms of their contributions to overall detection accuracy. Such results are useful in identifying the preferred operating regimes of each receiver. Conversely, given practical operating conditions and system constraints, they help to select the more effective receiver structure and guide the design of key system parameters.

In this paper, we propose a new two-stage (TS) structure for computationally efficient maximum-likelihood decoding (MLD) of linear block codes. With this structure, near optimal MLD performance can be achieved at low complexity through TS processing. The first stage of processing estimates a minimum sufficient set (MSS) of candidate codewords that contains the optimal codeword, while the second stage performs optimal or suboptimal decoding search within the estimated MSS of small size. Based on the new structure, we propose a decoding algorithm that systematically trades off between the decoding complexity and the bounded block error rate performance. A low-complexity complementary decoding algorithm is developed to estimate the MSS, followed by an ordered algebraic decoding (OAD) algorithm to achieve flexible system design. Since the size of the MSS changes with the signal-to-noise ratio, the overall decoding complexity adaptively scales with the quality of the communication link. Theoretical analysis is provided to evaluate the potential complexity reduction enabled by the proposed decoding structure.

Realizing the benefits of ultra-wideband (UWB) communications hinges critically on judicious pulse shape design to enable UWB spectral mask compatibility, and co-existence with and adaptation to other wireless devices. To this end, we propose a convex optimization based waveform design method for UWB radios. By casting the pulse design problem as a (convex) semidefinite program (SDP) over the pulse autocorrelation, globally optimal waveform designs can be efficiently obtained. While the focus of this paper is on the design of waveforms that optimally utilize the bandwidth and power allowed by the spectral mask, the flexibility of the SDP framework also allows the optimization of several other system objectives.

This work presents a maximum-likelihood (ML) decoding algorithm for linear block codes. In this algorithm, the optimal performance is achieved at low computational complexity through a two-stage processing. At the first stage, a minimum sufficient set S that includes the optimal solution is estimated. With the minimum sufficient set, the decoding complexity can be greatly reduced without performance degradation. At the second stage, ordered processing is performed within the estimated minimum sufficient set S to obtain the optimal solution. During the ordered processing, S is adoptively updated to minimize the computational complexity, and an effective stopping criterion is used to decide whether the optimal solution is found. Ordered processing not only helps to find the optimal solution quickly, but also enables simplified sub-optimal solutions with bounded block error rates. The proposed algorithm is also extended to decode block turbo codes. Finally, simulation results are given to show that this algorithm achieves optimal performance with a low average computational complexity.