260 research outputs found
Lightweight Boosting Models for User Response Prediction Using Adversarial Validation
The ACM RecSys Challenge 2023, organized by ShareChat, aims to predict the
probability of the app being installed. This paper describes the lightweight
solution to this challenge. We formulate the task as a user response prediction
task. For rapid prototyping for the task, we propose a lightweight solution
including the following steps: 1) using adversarial validation, we effectively
eliminate uninformative features from a dataset; 2) to address noisy continuous
features and categorical features with a large number of unique values, we
employ feature engineering techniques.; 3) we leverage Gradient Boosted
Decision Trees (GBDT) for their exceptional performance and scalability. The
experiments show that a single LightGBM model, without additional ensembling,
performs quite well. Our team achieved ninth place in the challenge with the
final leaderboard score of 6.059065. Code for our approach can be found here:
https://github.com/choco9966/recsys-challenge-2023.Comment: 7 pages, 4 figures, ACM RecSys 2023 Challenge Workshop accepted pape
Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services
In Location-Based Services(LBS), user behavior naturally has a strong
dependence on the spatiotemporal information, i.e., in different geographical
locations and at different times, user click behavior will change
significantly. Appropriate spatiotemporal enhancement modeling of user click
behavior and large-scale sparse attributes is key to building an LBS model.
Although most of existing methods have been proved to be effective, they are
difficult to apply to takeaway scenarios due to insufficient modeling of
spatiotemporal information. In this paper, we address this challenge by seeking
to explicitly model the timing and locations of interactions and proposing a
Spatiotemporal-Enhanced Network, namely StEN. In particular, StEN applies a
Spatiotemporal Profile Activation module to capture common spatiotemporal
preference through attribute features. A Spatiotemporal Preference Activation
is further applied to model the personalized spatiotemporal preference embodied
by behaviors in detail. Moreover, a Spatiotemporal-aware Target Attention
mechanism is adopted to generate different parameters for target attention at
different locations and times, thereby improving the personalized
spatiotemporal awareness of the model.Comprehensive experiments are conducted
on three large-scale industrial datasets, and the results demonstrate the
state-of-the-art performance of our methods. In addition, we have also released
an industrial dataset for takeaway industry to make up for the lack of public
datasets in this community.Comment: accepted by CIKM workshop 202
BARS: Towards Open Benchmarking for Recommender Systems
The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite significant progress made in both research
and practice of recommender systems, to date, there is a lack of a
widely-recognized benchmarking standard in this field. Many existing studies
perform model evaluations and comparisons in an ad-hoc manner, for example, by
employing their own private data splits or using different experimental
settings. Such conventions not only increase the difficulty in reproducing
existing studies, but also lead to inconsistent experimental results among
them. This largely limits the credibility and practical value of research
results in this field. To tackle these issues, we present an initiative project
(namely BARS) aiming for open benchmarking for recommender systems. In
comparison to some earlier attempts towards this goal, we take a further step
by setting up a standardized benchmarking pipeline for reproducible research,
which integrates all the details about datasets, source code, hyper-parameter
settings, running logs, and evaluation results. The benchmark is designed with
comprehensiveness and sustainability in mind. It covers both matching and
ranking tasks, and also enables researchers to easily follow and contribute to
the research in this field. This project will not only reduce the redundant
efforts of researchers to re-implement or re-run existing baselines, but also
drive more solid and reproducible research on recommender systems. We would
like to call upon everyone to use the BARS benchmark for future evaluation, and
contribute to the project through the portal at:
https://openbenchmark.github.io/BARS.Comment: Accepted by SIGIR 2022. Note that version v5 is updated to keep
consistency with the ACM camera-ready versio
Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
Using knowledge graphs to assist deep learning models in making
recommendation decisions has recently been proven to effectively improve the
model's interpretability and accuracy. This paper introduces an end-to-end deep
learning model, named RKGCN, which dynamically analyses each user's preferences
and makes a recommendation of suitable items. It combines knowledge graphs on
both the item side and user side to enrich their representations to maximize
the utilization of the abundant information in knowledge graphs. RKGCN is able
to offer more personalized and relevant recommendations in three different
scenarios. The experimental results show the superior effectiveness of our
model over 5 baseline models on three real-world datasets including movies,
books, and music
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
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