4,090 research outputs found
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the
presented items, plays a key role in online services. With two-decade rapid
development, nowadays the cumulated user behavior sequences on mature Internet
service platforms have become extremely long since the user's first
registration. Each user not only has intrinsic tastes, but also keeps changing
her personal interests during lifetime. Hence, it is challenging to handle such
lifelong sequential modeling for each individual user. Existing methodologies
for sequential modeling are only capable of dealing with relatively recent user
behaviors, which leaves huge space for modeling long-term especially lifelong
sequential patterns to facilitate user modeling. Moreover, one user's behavior
may be accounted for various previous behaviors within her whole online
activity history, i.e., long-term dependency with multi-scale sequential
patterns. In order to tackle these challenges, in this paper, we propose a
Hierarchical Periodic Memory Network for lifelong sequential modeling with
personalized memorization of sequential patterns for each user. The model also
adopts a hierarchical and periodical updating mechanism to capture multi-scale
sequential patterns of user interests while supporting the evolving user
behavior logs. The experimental results over three large-scale real-world
datasets have demonstrated the advantages of our proposed model with
significant improvement in user response prediction performance against the
state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets:
https://github.com/alimamarankgroup/HPM
Deep Character-Level Click-Through Rate Prediction for Sponsored Search
Predicting the click-through rate of an advertisement is a critical component
of online advertising platforms. In sponsored search, the click-through rate
estimates the probability that a displayed advertisement is clicked by a user
after she submits a query to the search engine. Commercial search engines
typically rely on machine learning models trained with a large number of
features to make such predictions. This is inevitably requires a lot of
engineering efforts to define, compute, and select the appropriate features. In
this paper, we propose two novel approaches (one working at character level and
the other working at word level) that use deep convolutional neural networks to
predict the click-through rate of a query-advertisement pair. Specially, the
proposed architectures only consider the textual content appearing in a
query-advertisement pair as input, and produce as output a click-through rate
prediction. By comparing the character-level model with the word-level model,
we show that language representation can be learnt from scratch at character
level when trained on enough data. Through extensive experiments using billions
of query-advertisement pairs of a popular commercial search engine, we
demonstrate that both approaches significantly outperform a baseline model
built on well-selected text features and a state-of-the-art word2vec-based
approach. Finally, by combining the predictions of the deep models introduced
in this study with the prediction of the model in production of the same
commercial search engine, we significantly improve the accuracy and the
calibration of the click-through rate prediction of the production system.Comment: SIGIR2017, 10 page
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
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