3,211 research outputs found
Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion
User post-click conversion prediction is of high interest to researchers and
developers. Recent studies employ multi-task learning to tackle the selection
bias and data sparsity problem, two severe challenges in post-click behavior
prediction, by incorporating click data. However, prior works mainly focused on
pointwise learning and the orders of labels (i.e., click and post-click) are
not well explored, which naturally poses a listwise learning problem. Inspired
by recent advances on differentiable sorting, in this paper, we propose a novel
multi-task framework that leverages orders of user behaviors to predict user
post-click conversion in an end-to-end approach. Specifically, we define an
aggregation operator to combine predicted outputs of different tasks to a
unified score, then we use the computed scores to model the label relations via
differentiable sorting. Extensive experiments on public and industrial datasets
show the superiority of our proposed model against competitive baselines.Comment: The paper is accepted as a short research paper by SIGIR 202
ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint
Large-scale online recommender system spreads all over the Internet being in
charge of two basic tasks: Click-Through Rate (CTR) and Post-Click Conversion
Rate (CVR) estimations. However, traditional CVR estimators suffer from
well-known Sample Selection Bias and Data Sparsity issues. Entire space models
were proposed to address the two issues via tracing the decision-making path of
"exposure_click_purchase". Further, some researchers observed that there are
purchase-related behaviors between click and purchase, which can better draw
the user's decision-making intention and improve the recommendation
performance. Thus, the decision-making path has been extended to
"exposure_click_in-shop action_purchase" and can be modeled with conditional
probability approach. Nevertheless, we observe that the chain rule of
conditional probability does not always hold. We report Probability Space
Confusion (PSC) issue and give a derivation of difference between ground-truth
and estimation mathematically. We propose a novel Entire Space Multi-Task Model
for Post-Click Conversion Rate via Parameter Constraint (ESMC) and two
alternatives: Entire Space Multi-Task Model with Siamese Network (ESMS) and
Entire Space Multi-Task Model in Global Domain (ESMG) to address the PSC issue.
Specifically, we handle "exposure_click_in-shop action" and "in-shop
action_purchase" separately in the light of characteristics of in-shop action.
The first path is still treated with conditional probability while the second
one is treated with parameter constraint strategy. Experiments on both offline
and online environments in a large-scale recommendation system illustrate the
superiority of our proposed methods over state-of-the-art models. The
real-world datasets will be released
Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations
Multi-task learning (MTL) has been successfully used in many real-world
applications, which aims to simultaneously solve multiple tasks with a single
model. The general idea of multi-task learning is designing kinds of global
parameter sharing mechanism and task-specific feature extractor to improve the
performance of all tasks. However, challenge still remains in balancing the
trade-off of various tasks since model performance is sensitive to the
relationships between them. Less correlated or even conflict tasks will
deteriorate the performance by introducing unhelpful or negative information.
Therefore, it is important to efficiently exploit and learn fine-grained
feature representation corresponding to each task. In this paper, we propose an
Adaptive Pattern Extraction Multi-task (APEM) framework, which is adaptive and
flexible for large-scale industrial application. APEM is able to fully utilize
the feature information by learning the interactions between the input feature
fields and extracted corresponding tasks-specific information. We first
introduce a DeepAuto Group Transformer module to automatically and efficiently
enhance the feature expressivity with a modified set attention mechanism and a
Squeeze-and-Excitation operation. Second, explicit Pattern Selector is
introduced to further enable selectively feature representation learning by
adaptive task-indicator vectors. Empirical evaluations show that APEM
outperforms the state-of-the-art MTL methods on public and real-world financial
services datasets. More importantly, we explore the online performance of APEM
in a real industrial-level recommendation scenario.Comment: 18 pages, 9 figure
Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey
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
Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection
Customer lifetime value (LTV) prediction is essential for mobile game
publishers trying to optimize the advertising investment for each user
acquisition based on the estimated worth. In mobile games, deploying
microtransactions is a simple yet effective monetization strategy, which
attracts a tiny group of game whales who splurge on in-game purchases. The
presence of such game whales may impede the practicality of existing LTV
prediction models, since game whales' purchase behaviours always exhibit varied
distribution from general users. Consequently, identifying game whales can open
up new opportunities to improve the accuracy of LTV prediction models. However,
little attention has been paid to applying game whale detection in LTV
prediction, and existing works are mainly specialized for the long-term LTV
prediction with the assumption that the high-quality user features are
available, which is not applicable in the UA stage. In this paper, we propose
ExpLTV, a novel multi-task framework to perform LTV prediction and game whale
detection in a unified way. In ExpLTV, we first innovatively design a deep
neural network-based game whale detector that can not only infer the intrinsic
order in accordance with monetary value, but also precisely identify high
spenders (i.e., game whales) and low spenders. Then, by treating the game whale
detector as a gating network to decide the different mixture patterns of LTV
experts assembling, we can thoroughly leverage the shared information and
scenario-specific information (i.e., game whales modelling and low spenders
modelling). Finally, instead of separately designing a purchase rate estimator
for two tasks, we design a shared estimator that can preserve the inner task
relationships. The superiority of ExpLTV is further validated via extensive
experiments on three industrial datasets
Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation
An accurate prediction of watch time has been of vital importance to enhance
user engagement in video recommender systems. To achieve this, there are four
properties that a watch time prediction framework should satisfy: first,
despite its continuous value, watch time is also an ordinal variable and the
relative ordering between its values reflects the differences in user
preferences. Therefore the ordinal relations should be reflected in watch time
predictions. Second, the conditional dependence between the video-watching
behaviors should be captured in the model. For instance, one has to watch half
of the video before he/she finishes watching the whole video. Third, modeling
watch time with a point estimation ignores the fact that models might give
results with high uncertainty and this could cause bad cases in recommender
systems. Therefore the framework should be aware of prediction uncertainty.
Forth, the real-life recommender systems suffer from severe bias amplifications
thus an estimation without bias amplification is expected. Therefore we propose
TPM for watch time prediction. Specifically, the ordinal ranks of watch time
are introduced into TPM and the problem is decomposed into a series of
conditional dependent classification tasks which are organized into a tree
structure. The expectation of watch time can be generated by traversing the
tree and the variance of watch time predictions is explicitly introduced into
the objective function as a measurement for uncertainty. Moreover, we
illustrate that backdoor adjustment can be seamlessly incorporated into TPM,
which alleviates bias amplifications. Extensive offline evaluations have been
conducted in public datasets and TPM have been deployed in a real-world video
app Kuaishou with over 300 million DAUs. The results indicate that TPM
outperforms state-of-the-art approaches and indeed improves video consumption
significantly
adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Ads
In this article, we present a general approach to personalizing ads through
encoding and learning from variable-length sequences of recent user actions and
diverse representations. To this end we introduce a three-component module
called the adSformer diversifiable personalization module (ADPM) that learns a
dynamic user representation. We illustrate the module's effectiveness and
flexibility by personalizing the Click-Through Rate (CTR) and Post-Click
Conversion Rate (PCCVR) models used in sponsored search. The first component of
the ADPM, the adSformer encoder, includes a novel adSformer block which learns
the most salient sequence signals. ADPM's second component enriches the learned
signal through visual, multimodal, and other pretrained representations.
Lastly, the third ADPM "learned on the fly" component further diversifies the
signal encoded in the dynamic user representation. The ADPM-personalized CTR
and PCCVR models, henceforth referred to as adSformer CTR and adSformer PCCVR,
outperform the CTR and PCCVR production baselines by and ,
respectively, in offline Area Under the Receiver Operating Characteristic Curve
(ROC-AUC). Following the robust online gains in A/B tests, Etsy Ads deployed
the ADPM-personalized sponsored search system to of traffic as of
February 2023
Digital user's decision journey
The landscape of the Internet is continually evolving. This creates huge opportunities for different industries to optimize vital channels online, resulting in various-forms of new Internet services. As a result, digital users are interacting with many digital systems and they are exhibiting dynamic behaviors. Their shopping behaviors are drastically different today than it used to be, with offline and online shopping interacting with each other. They have many channels to access online media but their consumption patterns on different channels are quite different. They do philanthropy online to help others but their heterogeneous motivations and different fundraising campaigns leads to distinct path-to-contribution. Understanding the digital user’s decision making process behind their dynamic behaviors is critical as they interact with various digital systems for the firms to improve user experience and improve their bottom line. In this thesis, I study digital users’ decision journeys and the corresponding digital technology firms’ strategies using inter-disciplinary approaches that combine econometrics, economic structural modeling and machine learning. The uncovered decision journey not only offer empirical managerial insights but also provide guideline for introducing intervention to better serve digital users
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