1,032 research outputs found
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
Discrete Conditional Diffusion for Reranking in Recommendation
Reranking plays a crucial role in modern multi-stage recommender systems by
rearranging the initial ranking list to model interplay between items.
Considering the inherent challenges of reranking such as combinatorial
searching space, some previous studies have adopted the evaluator-generator
paradigm, with a generator producing feasible sequences and a evaluator
selecting the best one based on estimated listwise utility. Inspired by the
remarkable success of diffusion generative models, this paper explores the
potential of diffusion models for generating high-quality sequences in
reranking. However, we argue that it is nontrivial to take diffusion models as
the generator in the context of recommendation. Firstly, diffusion models
primarily operate in continuous data space, differing from the discrete data
space of item permutations. Secondly, the recommendation task is different from
conventional generation tasks as the purpose of recommender systems is to
fulfill user interests. Lastly, real-life recommender systems require
efficiency, posing challenges for the inference of diffusion models. To
overcome these challenges, we propose a novel Discrete Conditional Diffusion
Reranking (DCDR) framework for recommendation. DCDR extends traditional
diffusion models by introducing a discrete forward process with tractable
posteriors, which adds noise to item sequences through step-wise discrete
operations (e.g., swapping). Additionally, DCDR incorporates a conditional
reverse process that generates item sequences conditioned on expected user
responses. Extensive offline experiments conducted on public datasets
demonstrate that DCDR outperforms state-of-the-art reranking methods.
Furthermore, DCDR has been deployed in a real-world video app with over 300
million daily active users, significantly enhancing online recommendation
quality
Automatic Music Playlist Generation via Simulation-based Reinforcement Learning
Personalization of playlists is a common feature in music streaming services,
but conventional techniques, such as collaborative filtering, rely on explicit
assumptions regarding content quality to learn how to make recommendations.
Such assumptions often result in misalignment between offline model objectives
and online user satisfaction metrics. In this paper, we present a reinforcement
learning framework that solves for such limitations by directly optimizing for
user satisfaction metrics via the use of a simulated playlist-generation
environment. Using this simulator we develop and train a modified Deep
Q-Network, the action head DQN (AH-DQN), in a manner that addresses the
challenges imposed by the large state and action space of our RL formulation.
The resulting policy is capable of making recommendations from large and
dynamic sets of candidate items with the expectation of maximizing consumption
metrics. We analyze and evaluate agents offline via simulations that use
environment models trained on both public and proprietary streaming datasets.
We show how these agents lead to better user-satisfaction metrics compared to
baseline methods during online A/B tests. Finally, we demonstrate that
performance assessments produced from our simulator are strongly correlated
with observed online metric results.Comment: 10 pages. KDD 2
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
Modern recommender systems lie at the heart of complex ecosystems that couple
the behavior of users, content providers, advertisers, and other actors.
Despite this, the focus of the majority of recommender research -- and most
practical recommenders of any import -- is on the local, myopic optimization of
the recommendations made to individual users. This comes at a significant cost
to the long-term utility that recommenders could generate for its users. We
argue that explicitly modeling the incentives and behaviors of all actors in
the system -- and the interactions among them induced by the recommender's
policy -- is strictly necessary if one is to maximize the value the system
brings to these actors and improve overall ecosystem "health". Doing so
requires: optimization over long horizons using techniques such as
reinforcement learning; making inevitable tradeoffs in the utility that can be
generated for different actors using the methods of social choice; reducing
information asymmetry, while accounting for incentives and strategic behavior,
using the tools of mechanism design; better modeling of both user and
item-provider behaviors by incorporating notions from behavioral economics and
psychology; and exploiting recent advances in generative and foundation models
to make these mechanisms interpretable and actionable. We propose a conceptual
framework that encompasses these elements, and articulate a number of research
challenges that emerge at the intersection of these different disciplines
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