Efficient Neural Query Auto Completion

Query Auto Completion (QAC), as the starting point of information retrieval tasks, is critical to user experience. Generally it has two steps: generating completed query candidates according to query prefixes, and ranking them based on extracted features. Three major challenges are observed for a query auto completion system: (1) QAC has a strict online latency requirement. For each keystroke, results must be returned within tens of milliseconds, which poses a significant challenge in designing sophisticated language models for it. (2) For unseen queries, generated candidates are of poor quality as contextual information is not fully utilized. (3) Traditional QAC systems heavily rely on handcrafted features such as the query candidate frequency in search logs, lacking sufficient semantic understanding of the candidate. In this paper, we propose an efficient neural QAC system with effective context modeling to overcome these challenges. On the candidate generation side, this system uses as much information as possible in unseen prefixes to generate relevant candidates, increasing the recall by a large margin. On the candidate ranking side, an unnormalized language model is proposed, which effectively captures deep semantics of queries. This approach presents better ranking performance over state-of-the-art neural ranking methods and reduces $\sim$95\% latency compared to neural language modeling methods. The empirical results on public datasets show that our model achieves a good balance between accuracy and efficiency. This system is served in LinkedIn job search with significant product impact observed.Comment: Accepted at CIKM 202

Collaborative Location Recommendation by Integrating Multi-dimensional Contextual Information

Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years. Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience. Traditionally, the most commonly used contextual information includes geographical and social context information. However, the increasing availability of check-in data makes it possible to design more effective location recommendation applications by modeling and integrating comprehensive types of contextual information, especially the temporal information. In this paper, we propose a collaborative filtering method based on Tensor Factorization, a generalization of the Matrix Factorization approach, to model the multi dimensional contextual information. Tensor Factorization naturally extends Matrix Factorization by increasing the dimensionality of concerns, within which the three-dimensional model is the one most popularly used. Our method exploits a high-order tensor to fuse heterogeneous contextual information about users’ check-ins instead of the traditional two dimensional user-location matrix. The factorization of this tensor leads to a more compact model of the data which is naturally suitable for integrating contextual information to make POI recommendations. Based on the model, we further improve the recommendation accuracy by utilizing the internal relations within users and locations to regularize the latent factors. Experimental results on a large real-world dataset demonstrate the effectiveness of our approach

Discovering overlapping groups in social media

Abstract—The increasing popularity of social media is shortening the distance between people. Social activities, e.g., tagging in Flickr, bookmarking in Delicious, twittering in Twitter, etc. are reshaping people’s social life and redefining their social roles. People with shared interests tend to form their groups in social media, and users within the same community likely exhibit similar social behavior (e.g., going for the same movies, having similar political viewpoints), which in turn reinforces the community structure. The multiple interactions in social activities entail that the community structures are often overlapping, i.e., one person is involved in several communities. We propose a novel co-clustering framework, which takes advantage of networking information between users and tags in social media, to discover these overlapping communities. In our method, users are connected via tags and tags are connected to users. This explicit representation of users and tags is useful for understanding group evolution by looking at who is interested in what. The efficacy of our method is supported by empirical evaluation in both synthetic and online social networking data

An Agent-Based Two-Stage Trading Model for Direct Electricity Procurement of Large Consumers

Many electricity markets around the world are still at developmental and transitional stages. To complete the transition and achieve the key objectives of perfect market design, designers often choose direct electricity procurement of large consumers (LCs) as a pilot. The trading mechanism is critical because it lays the foundation for the exploration of formulating a trading model and the succeeding solution; however, the existing trading mechanisms of direct electricity procurement struggle to cope with new challenges that electric power systems are facing. This paper proposes a novel two-stage trading mechanism, considering both the fairness and efficiency of direct electricity procurement. Based on the proposed trading mechanism, an agent-based trading model with multiple participants is developed. The simulation results of the transactions between LCs and generation companies (GenCos) illustrate the feasibility and effectiveness of the proposed mechanism. With this mechanism, LCs and GenCos will have more choices in the trading process and can benefit from the reduction of the average market price. The two-stage trading model provides a new choice for market designers and participants of direct electricity procurement

The Accelerated Inference of a Novel Optimized YOLOv5-LITE on Low-Power Devices for Railway Track Damage Detection

Railway track malfunctions can lead to severe consequences such as train derailments and collisions. Traditional manual inspection methods suffer from inaccuracies and low efficiency. Contemporary deep learning-based detection techniques have challenges in model accuracy, inference speed, and are often associated with expensive computational costs and high power consumption when deployed on devices. We propose an optimized lightweight network based on YOLOv5-lite. which employs an enhanced Fused Mobile Inverted Bottleneck Convolution (BF_MBConv) to reduce the number of parameters and floating-point operations (FLOP) during backbone feature extraction. The Squeeze-and-Excitation (SE) mechanism is adopted, emphasizing more critical track features by assigning different weights from a channel-wise perspective. Utilizing DropBlock with holistic dropping as a substitute for Dropout with random dropping offers a more efficient means of discarding redundant features. In the neck section, Shuffle convolution replaces the conventional one, significantly reducing the parameter count while better integrating feature information post-group convolution. Lastly, the incorporation of Focal-EIoU Loss augments regression, and with the application of incremental dataset processing techniques, it addresses accuracy and sample imbalance issues. The refined algorithm achieves a mean Average Precision (mAP)@0.5 of 94.4%, marking an 8.13% improvement over the original YOLOv5-lite. Moreover, by leveraging the embedded platform integrated with the Intel ® Movidius™ Neural Compute Stick cluster as the portable device for model deployment, Achieved a frame rate of 18.7 FPS. Our findings indicate that this approach can efficiently and accurately detect railway track damages. Additionally, it addresses the previously overlooked issues of performance-cost trade-offs, countering the past trend of prioritizing high performance at the expense of elevated power consumption and costs, proposing a harmonized approach that prioritizes efficiency and affordability