Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge. It is an important topic in recommendation due to the demand for multi-task prediction considering performance and efficiency. Although MTL has been well studied and developed, there is still a lack of systematic review in the recommendation community. To fill the gap, we provide a comprehensive review of existing multi-task deep recommender systems (MTDRS) in this survey. To be specific, the problem definition of MTDRS is first given, and it is compared with other related areas. Next, the development of MTDRS is depicted and the taxonomy is introduced from the task relation and methodology aspects. Specifically, the task relation is categorized into parallel, cascaded, and auxiliary with main, while the methodology is grouped into parameter sharing, optimization, and training mechanism. The survey concludes by summarizing the application and public datasets of MTDRS and highlighting the challenges and future directions of the field.

Multi-Task Deep Recommender Systems: A Survey

A review of federated learning: taxonomy, privacy and future directions

Multi-task learning has been widely applied in computational vision, natural language processing and other fields, which has achieved well performance. In recent years, a lot of work about multi-task learning recommender system has been yielded, but there is no previous literature to summarize these works. To bridge this gap, we provide a systematic literature survey about multi-task recommender systems, aiming to help researchers and practitioners quickly understand the current progress in this direction. In this survey, we first introduce the background and the motivation of the multi-task learning-based recommender systems. Then we provide a taxonomy of multi-task learning-based recommendation methods according to the different stages of multi-task learning techniques, which including task relationship discovery, model architecture and optimization strategy. Finally, we raise discussions on the application and promising future directions in this area.

Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey

Information fusion in crime event analysis: A decade survey on data, features and models

One of the most significant map services in navigation applications is route recommendation. However, most route recommendation systems only recommend trips based on time and distance, impacting quality-of-experience and route selection. This paper introduces a novel framework, namely MARRS, a multi-objective route recommendation system based on heterogeneous urban sensing open data (i.e., crime, accident, traffic flow, road network, meteorological, calendar event, and point of interest distributions). We introduce a wide, deep, and multitask-learning (WD-MTL) framework that uses a transformer to extract spatial, temporal, and semantic correlation for predicting crime, accident, and traffic flow of particular road segment. Later, for a particular source and destination, the adaptive epsilon constraint technique is used to optimize route satisfying multiple objective functions. The experimental results demonstrate the feasibility of figuring out the safest and efficient route selection.

MARRS: A Framework for multi-objective risk-aware route recommendation using Multitask-Transformer

Federated learning based driver recommendation for next generation transportation system

Urban anomalies are abnormal events such as a blocked driveway, illegal parking, noise, crime, crowd gathering, etc. affect people and policy managers drastically if not handled in time. Prediction of these anomalies in the early stages is critical for public safety and mitigation of economic losses. However, predicting urban anomalies has various challenges like complex spatio-temporal relationships, dynamic nature, and data sparsity. This paper proposes a novel end-to-end deep learning based framework, i.e., UApredictor that utilizes stacked spatial-temporal-interaction block to predict urban anomaly from multivariate time-series data. We model the problem using an attribute graph, where we represent city regions as nodes to capture inter region spatial information using a spatial transformer. Further, to capture temporal correlation, we utilize a temporal transformer, and the interaction module retains complex interaction between spatio-temporal dimensions. Besides, the attention layer is added on the top of the spatial-temporal-interaction block that captures important information for predicting urban anomaly. We use real-world NYC-Urban Anomaly, NYC-Taxi, NYC-POI, NYC-Road Network, NYC-Demographic, and NYC-Weather datasets of New York city to evaluate the urban anomaly prediction framework. The results show that our proposed framework predicts better in terms of F-measure, macro-F1, and micro-F1 than baseline and state-of-the-art models.

UApredictor: Urban Anomaly Prediction from Spatial-Temporal Data using Graph Transformer Neural Network

While the increased demand for taxi services like Uber, Lyft, Hailo, Ola, Grab, Cabify etc. provides livelihood to many drivers, the desire to raise income forces the drivers to work very hard without rest. However, continuous journeys not only affect their health, but also lead to abnormal driving behavior such as rash driving, swerving, side-slipping, sudden brakes, or weaving, leading to accidents in the worst cases. Motivated by the severity of rising accidents and health issues among drivers, this paper proposes a recommendation system, called RsSafe, for the safety of drivers. Aiming to improve the driving quality and the driver's experience, RsSafe suggests that the driver accepts or rejects the next trip based on the predicted driving behavior. In particular, we propose a fusion architecture that learns to predict the driver's behavior for the next trip using information from multiple streams. This architecture consists of Multi-task Learning with Attention (MTLA) that captures individual drivers' personality traits to deal with the adaptability of system. We use publicly available naturalistic driving behavior analysis dataset, namely the UAHDriveSet, results show that the MTLA predicts with F-measure score of 96%; and outperforms the baseline as well as state-of-the-art models.

Bhumika

Papers

MARRS: A Framework for multi-objective risk-aware route recommendation using Multitask-Transformer

Federated learning based driver recommendation for next generation transportation system

UApredictor: Urban Anomaly Prediction from Spatial-Temporal Data using Graph Transformer Neural Network

RsSafe: Personalized Driver Behavior Prediction for Safe Driving

Deep Learning Based Urban Anomaly Prediction from Spatiotemporal Data