Unbiased Learning to Rank with Unbiased Propensity Estimation

Learning to rank with biased click data is a well-known challenge. A variety of methods has been explored to debias click data for learning to rank such as click models, result interleaving and, more recently, the unbiased learning-to-rank framework based on inverse propensity weighting. Despite their differences, most existing studies separate the estimation of click bias (namely the \textit{propensity model}) from the learning of ranking algorithms. To estimate click propensities, they either conduct online result randomization, which can negatively affect the user experience, or offline parameter estimation, which has special requirements for click data and is optimized for objectives (e.g. click likelihood) that are not directly related to the ranking performance of the system. In this work, we address those problems by unifying the learning of propensity models and ranking models. We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank. Based on this observation, we propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker and an \textit{unbiased propensity model}. DLA is an automatic unbiased learning-to-rank framework as it directly learns unbiased ranking models from biased click data without any preprocessing. It can adapt to the change of bias distributions and is applicable to online learning. Our empirical experiments with synthetic and real-world data show that the models trained with DLA significantly outperformed the unbiased learning-to-rank algorithms based on result randomization and the models trained with relevance signals extracted by click models

Unconfounded Propensity Estimation for Unbiased Ranking

The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for optimizing learning-to-rank systems. Among existing solutions, automatic ULTR algorithms that jointly learn user bias models (i.e., propensity models) with unbiased rankers have received a lot of attention due to their superior performance and low deployment cost in practice. Despite their theoretical soundness, the effectiveness is usually justified under a weak logging policy, where the ranking model can barely rank documents according to their relevance to the query. However, when the logging policy is strong, e.g., an industry-deployed ranking policy, the reported effectiveness cannot be reproduced. In this paper, we first investigate ULTR from a causal perspective and uncover a negative result: existing ULTR algorithms fail to address the issue of propensity overestimation caused by the query-document relevance confounder. Then, we propose a new learning objective based on backdoor adjustment and highlight its differences from conventional propensity models, which reveal the prevalence of propensity overestimation. On top of that, we introduce a novel propensity model called Logging-Policy-aware Propensity (LPP) model and its distinctive two-step optimization strategy, which allows for the joint learning of LPP and ranking models within the automatic ULTR framework, and actualize the unconfounded propensity estimation for ULTR. Extensive experiments on two benchmarks demonstrate the effectiveness and generalizability of the proposed method.Comment: 11 pages, 5 figure

Learning with Weak Supervision for Email Intent Detection

Email remains one of the most frequently used means of online communication. People spend a significant amount of time every day on emails to exchange information, manage tasks and schedule events. Previous work has studied different ways for improving email productivity by prioritizing emails, suggesting automatic replies or identifying intents to recommend appropriate actions. The problem has been mostly posed as a supervised learning problem where models of different complexities were proposed to classify an email message into a predefined taxonomy of intents or classes. The need for labeled data has always been one of the largest bottlenecks in training supervised models. This is especially the case for many real-world tasks, such as email intent classification, where large scale annotated examples are either hard to acquire or unavailable due to privacy or data access constraints. Email users often take actions in response to intents expressed in an email (e.g., setting up a meeting in response to an email with a scheduling request). Such actions can be inferred from user interaction logs. In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails. We develop an end-to-end robust deep neural network model for email intent identification that leverages both clean annotated data and noisy weak supervision along with a self-paced learning mechanism. Extensive experiments on three different intent detection tasks show that our approach can effectively leverage the weakly supervised data to improve intent detection in emails.Comment: 10 pages, 3 figure

DEVELOPMENT FOR BREEDING PERFORMANCE MANAGEMENT SYSTEM ON PIG FARMS

Abstract The study was conducted to supply systemic and dynamic analysis data to support a better operation on a breeding pig farm with process management, especially in reproduction parameters. A full simulation model on a breeding pig farm running was proposed in the study, and a series of definitions of process parameters related to service performance, farrowing performance and weaning performance was put forward. Some of them are described on the calculating models. The relationship structural database was designed and a set of digital management information system was developed, based on proposed definitions and models by using Visual Basic 6.0 Access databases and Crystal report combined with genetic characteristic of different pig breeds. The System supplies a series of convenient , intelligent input interfaces of original datum, and all different reproduction data can be counted, analyzed and graphically shown, based on different performances in a specific duration, and it can dynamically derive out all sows history card that shows a complete reproduction performances including some important indexes such as farrowing rate, farrowing interval, average gestation days and average weaned weight et al. in terms of parities, which can be used to decide whether a female needs to be fell into disuse. Therefore, with the help of system analysis and software design techniques, the system made it possible to realize information management and intelligence analysis for a breeding pig farm based on whole digital management of reproduction process from services through weaning and among different categories of breeding pigs and parities

Traceability System of Pig-raising Process and Quality Safety on 3G

Abstract：In view of existed not flexibility and low efficiency in establishing feeding file of large-scale farms or farmer farms of pigs, by adopting intelligent PDA or mobile phone as application platform, combining with .Net 2005 language and SQL Server 2005 CE database as well as TD-SCDMA wireless wide band communication linking Internet as data transmission method，this study suggested data criterions on feeding process information collection of pigs, and developed a mobile PDA or phone system to track swine feeding process data, such as operators and main inputs, and to trace pork quality safety. The running of the system shows that it realized all kinds of data collecting and wireless submission including ear tag wearing and movements, immunity events, feeds and veterinary drugs used as well as casual inspection data，and also achieved remote data maintaining for pig′s feeding files and deepness inquiry to pork quality. The system not only makes up a deficiency from table data recording system for feeding file setting of a large-scale swine farm, but also is a kind of effective solution for farmer farm to set up swine feeding files. Furthermore, the system is a kind of mobile and convenient supervising tool to service official veterinarian to carry out their work. Finally, with the TD-SCDMA technology prevalence and communication fee decrease, the system will take part in a important role in constructing Chinese pork quality safety traceability system

Traceability System of Pig-Raising Process and Quality Safety on 3G

Part 1: Decision Support Systems, Intelligent Systems and Artificial Intelligence ApplicationsInternational audienceIn view of existed not flexibility and low efficiency in establishing feeding file of large-scale farms or farmer farms of pigs, by adopting intelligent PDA or mobile phone as application platform, combining with .Net 2005 language and SQL Server 2005 CE database as well as TD-SCDMA wireless wide band communication linking Internet as data transmission method, this study suggested data criterions on feeding process information collection of pigs, and developed a mobile PDA or phone system to track swine feeding process data, such as operators and main inputs, and to trace pork quality safety. The running of the system shows that it realized all kinds of data collecting and wireless submission including ear tag wearing and movements, immunity events, feeds and veterinary drugs used as well as casual inspection data, and also achieved remote data maintaining for pig’s feeding files and deepness inquiry to pork quality. The system not only makes up a deficiency from table data recording system for feeding file setting of a large-scale swine farm, but also is a kind of effective solution for farmer farm to set up swine feeding files. Furthermore, the system is a kind of mobile and convenient supervising tool to service official veterinarian to carry out their work. Finally, with the TDSCDMA technology prevalence and communication fee decrease, the system will take part in a important role in constructing Chinese pork quality safety traceability system

A Study on Pig Slaughter Traceability Solution Based on RFID

International audienceDue to an assembly line production and poor environment conditions in pig slaughterhouses, collection of slaughter tracing information is not a simple thing. Based on the UHF radio frequency identification (RFID) technologies, this study designed a RFID tag for carcass, a RS232-PS2 data conversion line and some data norms such as the RFID carcass tag and partition meat label norm, and developed online reading and writing system for RFID tags, accomplished RFID identification for carcass and automatic identification on the slaughter line. Through identifying ear tags at the pig heading process, fixing RFID carcass tags at the half-carcass process, printing partition labels at pork exclusive stores, the study were able not only to collect, transmit and deal traceability information for pig slaughter in the key processes of the whole pig slaughter line, but also print a set of commercial cutting meat tags of 1D bar code based RFID carcass tag in the sales store. This study has been applied for demonstration in Tianjin and explored any possibility for application of RFID technology in pork quality traceability system from both technology and application links

Quantitative Detection of Mastitis Factor IL-6 in Dairy Cow Using the SERS Improved Immunofiltration Assay

Interleukin-6 (IL-6) is generally used as a biomarker for the evaluation of inflammatory infection in humans and animals. However, there is no approach for the on-site and rapid detection of IL-6 for the monitoring of mastitis in dairy farm scenarios. A rapid and highly sensitive surface enhanced Raman scattering (SERS) immunofiltration assay (IFA) for IL-6 detection was developed in the present study. In this assay, a high sensitivity gold core silver shell SERS nanotag with Raman molecule 4-mercaptobenzoic acid (4-MBA) embedded into the gap was fabricated for labelling. Through the immuno-specific combination of the antigen and antibody, antibody conjugated SERS nanotags were captured on the test zone, which facilitated the SERS measurement. The quantitation of IL-6 was performed by the readout Raman signal in the test region. The results showed that the detection limit (LOD) of IL-6 in milk was 0.35 pg mL−1, which was far below the threshold value of 254.32 pg mL−1. The recovery of the spiking experiment was 87.0–102.7%, with coefficients of variation below 9.0% demonstrating high assay accuracy and precision. We believe the immunosensor developed in the current study could be a promising tool for the rapid assessment of mastitis by detecting milk IL-6 in dairy cows. Moreover, this versatile immunosensor could also be applied for the detection of a wide range of analytes in dairy cow healthy monitoring

DNAzyme-Amplified Electrochemical Biosensor Coupled with pH Meter for Ca²⁺ Determination at Variable pH Environments

For more than 50% of multiparous cows, it is difficult to adapt to the sudden increase in calcium demand for milk production, which is highly likely to cause hypocalcemia. An electrochemical biosensor is a portable and efficient method to sense Ca2+ concentrations, but biomaterial is easily affected by the pH of the analyte solution. Here, an electrochemical biosensor was fabricated using a glassy carbon electrode (GCE) and single-walled carbon nanotube (SWNT), which amplified the impedance signal by changing the structure and length of the DNAzyme. Aiming at the interference of the pH, the electrochemical biosensor (GCE/SWNT/DNAzyme) was coupled with a pH meter to form an electrochemical device. It was used to collect data at different Ca2+ concentrations and pH values, and then was processed using different mathematical models, of which GPR showed higher detecting accuracy. After optimizing the detecting parameters, the electrochemical device could determine the Ca2+ concentration ranging from 5 μM to 25 mM, with a detection limit of 4.2 μM at pH values ranging from 4.0 to 7.5. Finally, the electrochemical device was used to determine the Ca2+ concentrations in different blood and milk samples, which can overcome the influence of the pH