Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

An Axiomatic Analysis of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility Metric

Many evaluation metrics have been defined to evaluate the effectiveness ad-hoc retrieval and search result diversification systems. However, it is often unclear which evaluation metric should be used to analyze the performance of retrieval systems given a specific task. Axiomatic analysis is an informative mechanism to understand the fundamentals of metrics and their suitability for particular scenarios. In this paper, we define a constraint-based axiomatic framework to study the suitability of existing metrics in search result diversification scenarios. The analysis informed the definition of Rank-Biased Utility (RBU) -- an adaptation of the well-known Rank-Biased Precision metric -- that takes into account redundancy and the user effort associated to the inspection of documents in the ranking. Our experiments over standard diversity evaluation campaigns show that the proposed metric captures quality criteria reflected by different metrics, being suitable in the absence of knowledge about particular features of the scenario under study.Comment: Original version: 10 pages. Preprint of full paper to appear at SIGIR'18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, July 8-12, 2018, Ann Arbor, MI, USA. ACM, New York, NY, US

Retrieve Anything To Augment Large Language Models

Large language models (LLMs) face significant challenges stemming from their inherent limitations in knowledge, memory, alignment, and action. These challenges cannot be addressed by LLMs alone, but should rely on assistance from the external world, such as knowledge base, memory store, demonstration examples, and tools. Retrieval augmentation stands as a vital mechanism for bridging the gap between LLMs and the external assistance. However, conventional methods encounter two pressing issues. On the one hand, the general-purpose retrievers are not properly optimized for the retrieval augmentation of LLMs. On the other hand, the task-specific retrievers lack the required versatility, hindering their performance across the diverse retrieval augmentation scenarios. In this work, we present a novel approach, the LLM-Embedder, which comprehensively supports the diverse retrieval augmentation needs of LLMs with one unified embedding model. Training such a unified model is non-trivial, as various retrieval tasks aim to capture distinct semantic relationships, often subject to mutual interference. To address this challenge, we systematically optimize our training methodology. This includes reward formulation based on LLMs' feedback, the stabilization of knowledge distillation, multi-task fine-tuning with explicit instructions, and homogeneous in-batch negative sampling. These optimization strategies contribute to the outstanding empirical performance of the LLM-Embedder. Notably, it yields remarkable enhancements in retrieval augmentation for LLMs, surpassing both general-purpose and task-specific retrievers in various evaluation scenarios. Our checkpoint and source code are publicly available at https://github.com/FlagOpen/FlagEmbedding

Experience-driven Control For Networking And Computing

Modern networking and computing systems have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this thesis, we aim to study system control problems from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL), to develop experience-driven model-free approaches, which enable a network or a device to learn the best way to control itself from its own experience (e.g., runtime statistics data) rather than from accurate mathematical models, just as a human learns a new skill (e.g., driving, swimming, etc). To demonstrate the feasibility and superiority of this experience-driven control design philosophy, we present the design, implementation, and evaluation of multiple DRL-based control frameworks on two fundamental networking problems, Traffic Engineering (TE) and Multi-Path TCP (MPTCP) congestion control, as well as one cutting-edge application, resource co-scheduling for Deep Neural Network (DNN) models on mobile and edge devices with heterogeneous hardware. We first propose DRL-TE, a DRL-based framework that enables experience-driven networking for TE. DRL-TE maximizes a widely-used utility function by jointly learning network environment and its dynamics, and making decisions under the guidance of powerful DNNs. We propose two new techniques, TE-aware exploration and actor-critic-based prioritized experience replay, to optimize the general DRL framework particularly for TE. Furthermore, we propose an Actor-Critic-based Transfer learning framework for TE, ACT-TE, which solves a practical problem in experience-driven networking: when network configurations are changed, how to train a new DRL agent to effectively and quickly adapt to the new environment. In the new network environment, ACT-TE leverages policy distillation to rapidly learn a new control policy from both old knowledge (i.e., distilled from the existing agent) and new experience (i.e., newly collected samples). In addition, we propose DRL-CC to enable experience-driven congestion control for MPTCP. DRL-CC utilizes a single (instead of multiple independent) DRL agent to dynamically and jointly perform congestion control for all active MPTCP flows on an end host with the objective of maximizing the overall utility. The novelty of our design is to utilize a flexible recurrent neural network, LSTM, under a DRL framework for learning a representation for all active flows and dealing with their dynamics. Moreover, we integrate the above LSTM-based representation network into an actor-critic framework for continuous congestion control, which applies the deterministic policy gradient method to train actor, critic, and LSTM networks in an end-to-end manner. With the emergence of more and more powerful chipsets and hardware and the rise of Artificial Intelligence of Things (AIoT), there is a growing trend for bringing DNN models to empower mobile and edge devices with intelligence such that they can support attractive AI applications on the edge in a real-time or near real-time manner. To leverage heterogeneous computational resources (such as CPU, GPU, DSP, etc) to effectively and efficiently support concurrent inference of multiple DNN models on a mobile or edge device, in the last part of this thesis, we propose a novel experience-driven control framework for resource co-scheduling, which we call COSREL. COSREL has the following desirable features: 1) it achieves significant speedup over commonly-used methods by efficiently utilizing all the computational resources on heterogeneous hardware; 2) it leverages DRL to make dynamic and wise online scheduling decisions based on system runtime state; 3) it is capable of making a good tradeoff among inference latency, throughput and energy efficiency; and 4) it makes no changes to given DNN models, thus preserves their accuracies. To validate and evaluate the proposed frameworks, we conduct extensive experiments on packet-level simulation (for TE), testbed with modified Linux kernel (for MPTCP), and off-the-shelf Android devices (for resource co-scheduling). The results well justify the effectiveness of these frameworks, as well as their superiority over several baseline methods

From Knowledge Augmentation to Multi-tasking: Towards Human-like Dialogue Systems

The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.Comment: PhD thesi

Tracer Applications of Noble Gas Radionuclides in the Geosciences

The noble gas radionuclides, including 81Kr (half-life = 229,000 yr), 85Kr (11 yr), and 39Ar (269 yr), possess nearly ideal chemical and physical properties for studies of earth and environmental processes. Recent advances in Atom Trap Trace Analysis (ATTA), a laser-based atom counting method, have enabled routine measurements of the radiokrypton isotopes, as well as the demonstration of the ability to measure 39Ar in environmental samples. Here we provide an overview of the ATTA technique, and a survey of recent progress made in several laboratories worldwide. We review the application of noble gas radionuclides in the geosciences and discuss how ATTA can help advance these fields, specifically determination of groundwater residence times using 81Kr, 85Kr, and 39Ar; dating old glacial ice using 81Kr; and an 39Ar survey of the main water masses of the oceans, to study circulation pathways and estimate mean residence times. Other scientific questions involving deeper circulation of fluids in the Earth's crust and mantle also are within the scope of future applications. We conclude that the geoscience community would greatly benefit from an ATTA facility dedicated to this field, with instrumentation for routine measurements, as well as for research on further development of ATTA methods

Workshop Report: Developing a Research Agenda for the Energy Water Nexus

The energy water nexus has attracted public scrutiny because of the concerns about their interdependence and the possibility for cascading vulnerabilities from one system to the other. There are trends toward more water-­‐intensive energy (such as biofuels , unconventional oil and gas production, and regulations driving more water consumption for thermoelectric power production ) and more energy-­‐intensive water (such as desalination, or deeper ground water pumping and production). In addition demographic trends of population and economic growth will likely drive up total and per capita water and energy demand, and due to climate change related distortions of the hydrologic cycle, it is expected that the existing interdependencies will be come even more of a concern. Therefore, developing a research agenda and strategy to mitigate potential vulnerabilities and to meet economic and environmental targets for efficiently using energy and water would be very worthwhile. To address these concerns, the National Science Foundation (NSF) sponsored a workshop on June 10-­‐11, 2013 in Arlington, VA (at NSF headquarters) to bring together technical, academic, and industry experts from across the country to help develop such a research agenda. The workshop was sponsored by NSF Grant Number CBET 1341032 from the Division of Chemical, Bioengineering, Environmental and Transport Systems. Supporting programs were: Thermal Transport Processes, Environmental Sustainability, and Environmental Engineering.Center for Research in Water Resource

Heterogeneous resource management in energy hubs with self-consumption: Contributions and application example

The energy hub concept and modeling methodology are widely employed tools for solving resource conversionand storage scheduling problems. For instance, industrial clusters might benefit from determining the suitabletime to operate their facilities and to sell electricity to the public power grid, according to legal, economic orenvironmental factors. In this paper, novel elements are introduced in order to more accurately represent realplants and to reduce the amount of decision variables. The major innovation is to consider devices consuming aresource which is not related to the quantity of output produced, by attaching binary decision variables tocertain energy hub outputs. Secondly, a path vector is defined to take into account the flows of resources withinthe system instead of employing a variable for each branch between the components. The third innovationconsists of an additional vector to express the amount of output resources sold from the energy hub, includingconstraints for those resources which are exported and imported through the same medium. An extended energyhub model is first proposed and then applied to a real plant example, including multiple and heterogeneousresources and performing a comparison between days with different demands, weather conditions and electricityprices. The results obtained in the selected scenarios demonstrate a logical operation scheduling, and thereforevalidate the proposed approach