6,864 research outputs found
You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction
In the cost per click (CPC) pricing model, an advertiser pays an ad network
only when a user clicks on an ad; in turn, the ad network gives a share of that
revenue to the publisher where the ad was impressed. Still, advertisers may be
unsatisfied with ad networks charging them for "valueless" clicks, or so-called
accidental clicks. [...] Charging advertisers for such clicks is detrimental in
the long term as the advertiser may decide to run their campaigns on other ad
networks. In addition, machine-learned click models trained to predict which ad
will bring the highest revenue may overestimate an ad click-through rate, and
as a consequence negatively impacting revenue for both the ad network and the
publisher. In this work, we propose a data-driven method to detect accidental
clicks from the perspective of the ad network. We collect observations of time
spent by users on a large set of ad landing pages - i.e., dwell time. We notice
that the majority of per-ad distributions of dwell time fit to a mixture of
distributions, where each component may correspond to a particular type of
clicks, the first one being accidental. We then estimate dwell time thresholds
of accidental clicks from that component. Using our method to identify
accidental clicks, we then propose a technique that smoothly discounts the
advertiser's cost of accidental clicks at billing time. Experiments conducted
on a large dataset of ads served on Yahoo mobile apps confirm that our
thresholds are stable over time, and revenue loss in the short term is
marginal. We also compare the performance of an existing machine-learned click
model trained on all ad clicks with that of the same model trained only on
non-accidental clicks. There, we observe an increase in both ad click-through
rate (+3.9%) and revenue (+0.2%) on ads served by the Yahoo Gemini network when
using the latter. [...
Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
In many industrial applications like online advertising and recommendation
systems, diverse and accurate user profiles can greatly help improve
personalization. For building user profiles, deep learning is widely used to
mine expressive tags to describe users' preferences from their historical
actions. For example, tags mined from users' click-action history can represent
the categories of ads that users are interested in, and they are likely to
continue being clicked in the future. Traditional solutions usually introduce
multiple independent Two-Tower models to mine tags from different actions,
e.g., click, conversion. However, the models cannot learn complementarily and
support effective training for data-sparse actions. Besides, limited by the
lack of information fusion between the two towers, the model learning is
insufficient to represent users' preferences on various topics well. This paper
introduces a novel multi-task model called Mixture of Virtual-Kernel Experts
(MVKE) to learn multiple topic-related user preferences based on different
actions unitedly. In MVKE, we propose a concept of Virtual-Kernel Expert, which
focuses on modeling one particular facet of the user's preference, and all of
them learn coordinately. Besides, the gate-based structure used in MVKE builds
an information fusion bridge between two towers, improving the model's
capability much and maintaining high efficiency. We apply the model in Tencent
Advertising System, where both online and offline evaluations show that our
method has a significant improvement compared with the existing ones and brings
about an obvious lift to actual advertising revenue.Comment: 10 pages, under revie
LT4REC:A Lottery Ticket Hypothesis Based Multi-task Practice for Video Recommendation System
Click-through rate prediction (CTR) and post-click conversion rate prediction
(CVR) play key roles across all industrial ranking systems, such as
recommendation systems, online advertising, and search engines. Different from
the extensive research on CTR, there is much less research on CVR estimation,
whose main challenge is extreme data sparsity with one or two orders of
magnitude reduction in the number of samples than CTR. People try to solve this
problem with the paradigm of multi-task learning with the sufficient samples of
CTR, but the typical hard sharing method can't effectively solve this problem,
because it is difficult to analyze which parts of network components can be
shared and which parts are in conflict, i.e., there is a large inaccuracy with
artificially designed neurons sharing. In this paper, we model CVR in a
brand-new method by adopting the lottery-ticket-hypothesis-based sparse sharing
multi-task learning, which can automatically and flexibly learn which neuron
weights to be shared without artificial experience. Experiments on the dataset
gathered from traffic logs of Tencent video's recommendation system demonstrate
that sparse sharing in the CVR model significantly outperforms competitive
methods. Due to the nature of weight sparsity in sparse sharing, it can also
significantly reduce computational complexity and memory usage which are very
important in the industrial recommendation system.Comment: 6 pages,4 figure
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection
Preparation and decay of a single quantum of vibration at ambient conditions
A single quantum of excitation of a mechanical oscillator is a textbook
example of the principles of quantum physics. Mechanical oscillators, despite
their pervasive presence in nature and modern technology, do not generically
exist in an excited Fock state. In the past few years, careful isolation of
GHz-frequency nano-scale oscillators has allowed experimenters to prepare such
states at milli-Kelvin temperatures. These developments illustrate the tension
between the basic predictions of quantum mechanics that should apply to all
mechanical oscillators existing even at ambient conditions, and the complex
experiments in extreme conditions required to observe those predictions. We
resolve the tension by creating a single Fock state of a vibration mode of a
crystal at room temperature using a technique that can be applied to any
Raman-active system. After exciting a bulk diamond with a femtosecond laser
pulse and detecting a Stokes-shifted photon, the 40~THz Raman-active internal
vibrational mode is prepared in the Fock state with probability.
The vibrational state is read out by a subsequent pulse, which when subjected
to a Hanbury-Brown-Twiss intensity correlation measurement reveals the
sub-Poisson number statistics of the vibrational mode. By controlling the delay
between the two pulses we are able to witness the decay of the vibrational Fock
state over its ps lifetime at room temperature. Our technique is agnostic
to specific selection rules, and should thus be applicable to any Raman-active
medium, opening a new generic approach to the experimental study of quantum
effects related to vibrational degrees of freedom in molecules and solid-state
systems
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Articular human joint modelling
Copyright @ Cambridge University Press 2009.The work reported in this paper encapsulates the theories and algorithms developed to drive the core analysis modules of the software which has been developed to model a musculoskeletal structure of anatomic joints. Due to local bone surface and contact geometry based joint kinematics, newly developed algorithms make the proposed modeller different from currently available modellers. There are many modellers that are capable of modelling gross human body motion. Nevertheless, none of the available modellers offer complete elements of joint modelling. It appears that joint modelling is an extension of their core analysis capability, which, in every case, appears to be musculoskeletal motion dynamics. It is felt that an analysis framework that is focused on human joints would have significant benefit and potential to be used in many orthopaedic applications. The local mobility of joints has a significant influence in human motion analysis, in understanding of joint loading, tissue behaviour and contact forces. However, in order to develop a bone surface based joint modeller, there are a number of major problems, from tissue idealizations to surface geometry discretization and non-linear motion analysis. This paper presents the following: (a) The physical deformation of biological tissues as linear or non-linear viscoelastic deformation, based on spring-dashpot elements. (b) The linear dynamic multibody modelling, where the linear formulation is established for small motions and is particularly useful for calculating the equilibrium position of the joint. This model can also be used for finding small motion behaviour or loading under static conditions. It also has the potential of quantifying the joint laxity. (c) The non-linear dynamic multibody modelling, where a non-matrix and algorithmic formulation is presented. The approach allows handling complex material and geometrical nonlinearity easily. (d) Shortest path algorithms for calculating soft tissue line of action geometries. The developed algorithms are based on calculating minimum ‘surface mass’ and ‘surface covariance’. An improved version of the ‘surface covariance’ algorithm is described as ‘residual covariance’. The resulting path is used to establish the direction of forces and moments acting on joints. This information is needed for linear or non-linear treatment of the joint motion. (e) The final contribution of the paper is the treatment of the collision. In the virtual world, the difficulty in analysing bodies in motion arises due to body interpenetrations. The collision algorithm proposed in the paper involves finding the shortest projected ray from one body to the other. The projection of the body is determined by the resultant forces acting on it due to soft tissue connections under tension. This enables the calculation of collision condition of non-convex objects accurately. After the initial collision detection, the analysis involves attaching special springs (stiffness only normal to the surfaces) at the ‘potentially colliding points’ and motion of bodies is recalculated. The collision algorithm incorporates the rotation as well as translation. The algorithm continues until the joint equilibrium is achieved. Finally, the results obtained based on the software are compared with experimental results obtained using cadaveric joints
Solid oxide fuel cell reactor analysis and optimisation through a novel multi-scale modelling strategy
The simulation of a solid oxide fuel cell (SOFC) that incorporates a detailed user-developed model was performed within the commercial flowsheet simulator Aspen Plus. It allows modification of the SOFC's governing equations, as well as the configuration of the cell's fuel-air flow pattern at the flowsheet level. Initially, the dynamic behaviour of single compartment of a cell was examined with a 0D model, which became the building block for more complex SOFC configurations. Secondly, a sensitivity analysis was performed at the channel (1D) scale for different flow patterns. Thirdly, the effect of fuel and air flow rates on the predominant distributed variables of a cell was tested on a 2D assembly. Finally, an optimisation study was carried out on the 2D cell, leading to a robust, optimal air distribution profile that minimises the internal temperature gradient. This work forms the foundation of future stack and system scale studies
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