9,851 research outputs found
SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning
Session-based recommendations aim to predict the next behavior of users based
on ongoing sessions. The previous works have been modeling the session as a
variable-length of a sequence of items and learning the representation of both
individual items and the aggregated session. Recent research has applied graph
neural networks with an attention mechanism to capture complicated item
transitions and dependencies by modeling the sessions into graph-structured
data. However, they still face fundamental challenges in terms of data and
learning methodology such as sparse supervision signals and noisy interactions
in sessions, leading to sub-optimal performance. In this paper, we propose
SR-GCL, a novel contrastive learning framework for a session-based
recommendation. As a crucial component of contrastive learning, we propose two
global context enhanced data augmentation methods while maintaining the
semantics of the original session. The extensive experiment results on two
real-world E-commerce datasets demonstrate the superiority of SR-GCL as
compared to other state-of-the-art methods.Comment: 11 pages. This paper has been accepted by DLG-AAAI'2
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
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of
sequential recommendation. Existing neural sequential recommenders typically
adopt a generative way trained with Maximum Likelihood Estimation (MLE). When
context information (called factor) is involved, it is difficult to analyze
when and how each individual factor would affect the final recommendation
performance. For this purpose, we take a new perspective and introduce
adversarial learning to sequential recommendation. In this paper, we present a
Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the
effect of context information on sequential recommendation. Specifically, our
proposed MFGAN has two kinds of modules: a Transformer-based generator taking
user behavior sequences as input to recommend the possible next items, and
multiple factor-specific discriminators to evaluate the generated sub-sequence
from the perspectives of different factors. To learn the parameters, we adopt
the classic policy gradient method, and utilize the reward signal of
discriminators for guiding the learning of the generator. Our framework is
flexible to incorporate multiple kinds of factor information, and is able to
trace how each factor contributes to the recommendation decision over time.
Extensive experiments conducted on three real-world datasets demonstrate the
superiority of our proposed model over the state-of-the-art methods, in terms
of effectiveness and interpretability
Large Language Models for Intent-Driven Session Recommendations
Intent-aware session recommendation (ISR) is pivotal in discerning user
intents within sessions for precise predictions. Traditional approaches,
however, face limitations due to their presumption of a uniform number of
intents across all sessions. This assumption overlooks the dynamic nature of
user sessions, where the number and type of intentions can significantly vary.
In addition, these methods typically operate in latent spaces, thus hinder the
model's transparency.Addressing these challenges, we introduce a novel ISR
approach, utilizing the advanced reasoning capabilities of large language
models (LLMs). First, this approach begins by generating an initial prompt that
guides LLMs to predict the next item in a session, based on the varied intents
manifested in user sessions. Then, to refine this process, we introduce an
innovative prompt optimization mechanism that iteratively self-reflects and
adjusts prompts. Furthermore, our prompt selection module, built upon the LLMs'
broad adaptability, swiftly selects the most optimized prompts across diverse
domains. This new paradigm empowers LLMs to discern diverse user intents at a
semantic level, leading to more accurate and interpretable session
recommendations. Our extensive experiments on three real-world datasets
demonstrate the effectiveness of our method, marking a significant advancement
in ISR systems
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
Session-based recommendation (SBR) systems aim to utilize the user's
short-term behavior sequence to predict the next item without the detailed user
profile. Most recent works try to model the user preference by treating the
sessions as between-item transition graphs and utilize various graph neural
networks (GNNs) to encode the representations of pair-wise relations among
items and their neighbors. Some of the existing GNN-based models mainly focus
on aggregating information from the view of spatial graph structure, which
ignores the temporal relations within neighbors of an item during message
passing and the information loss results in a sub-optimal problem. Other works
embrace this challenge by incorporating additional temporal information but
lack sufficient interaction between the spatial and temporal patterns. To
address this issue, inspired by the uniformity and alignment properties of
contrastive learning techniques, we propose a novel framework called
Session-based Recommendation with Spatio-Temporal Contrastive Learning Enhanced
GNNs (RESTC). The idea is to supplement the GNN-based main supervised
recommendation task with the temporal representation via an auxiliary
cross-view contrastive learning mechanism. Furthermore, a novel global
collaborative filtering graph (CFG) embedding is leveraged to enhance the
spatial view in the main task. Extensive experiments demonstrate the
significant performance of RESTC compared with the state-of-the-art baselines
e.g., with an improvement as much as 27.08% gain on HR@20 and 20.10% gain on
[email protected]: Under reviewing draft of ACM TOI
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