3,822 research outputs found
Self Contrastive Learning for Session-based Recommendation
Session-based recommendation, which aims to predict the next item of users'
interest as per an existing sequence interaction of items, has attracted
growing applications of Contrastive Learning (CL) with improved user and item
representations. However, these contrastive objectives: (1) serve a similar
role as the cross-entropy loss while ignoring the item representation space
optimisation; and (2) commonly require complicated modelling, including complex
positive/negative sample constructions and extra data augmentation. In this
work, we introduce Self-Contrastive Learning (SCL), which simplifies the
application of CL and enhances the performance of state-of-the-art CL-based
recommendation techniques. Specifically, SCL is formulated as an objective
function that directly promotes a uniform distribution among item
representations and efficiently replaces all the existing contrastive objective
components of state-of-the-art models. Unlike previous works, SCL eliminates
the need for any positive/negative sample construction or data augmentation,
leading to enhanced interpretability of the item representation space and
facilitating its extensibility to existing recommender systems. Through
experiments on three benchmark datasets, we demonstrate that SCL consistently
improves the performance of state-of-the-art models with statistical
significance. Notably, our experiments show that SCL improves the performance
of two best-performing models by 8.2% and 9.5% in P@10 (Precision) and 9.9% and
11.2% in MRR@10 (Mean Reciprocal Rank) on average across different benchmarks.
Additionally, our analysis elucidates the improvement in terms of alignment and
uniformity of representations, as well as the effectiveness of SCL with a low
computational cost.Comment: Technical Repor
Dual-Ganularity Contrastive Learning for Session-based Recommendation
Session-based recommendation systems(SBRS) are more suitable for the current
e-commerce and streaming media recommendation scenarios and thus have become a
hot topic. The data encountered by SBRS is typically highly sparse, which also
serves as one of the bottlenecks limiting the accuracy of recommendations. So
Contrastive Learning(CL) is applied in SBRS owing to its capability of
improving embedding learning under the condition of sparse data. However,
existing CL strategies are limited in their ability to enforce finer-grained
(e.g., factor-level) comparisons and, as a result, are unable to capture subtle
differences between instances. More than that, these strategies usually use
item or segment dropout as a means of data augmentation which may result in
sparser data and thus ineffective self-supervised signals. By addressing the
two aforementioned limitations, we introduce a novel multi-granularity CL
framework. Specifically, two extra augmented embedding convolution channels
with different granularities are constructed and the embeddings learned by them
are compared with those learned from original view to complete the CL tasks. At
factor-level, we employ Disentangled Representation Learning to obtain
finer-grained data(e.g. factor-level embeddings), with which we can construct
factor-level convolution channels. At item-level, the star graph is deployed as
the augmented data and graph convolution on it can ensure the effectiveness of
self-supervised signals. Compare the learned embeddings of these two views with
the learned embeddings of the basic view to achieve CL at two granularities.
Finally, the more precise item-level and factor-level embeddings obtained are
referenced to generate personalized recommendations for the user. The proposed
model is validated through extensive experiments on two benchmark datasets,
showcasing superior performance compared to existing methods
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
Session-based recommendation (SBR) focuses on next-item prediction at a
certain time point. As user profiles are generally not available in this
scenario, capturing the user intent lying in the item transitions plays a
pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the
item transitions as pairwise relations, which neglect the complex high-order
information among items. Hypergraph provides a natural way to capture
beyond-pairwise relations, while its potential for SBR has remained unexplored.
In this paper, we fill this gap by modeling session-based data as a hypergraph
and then propose a hypergraph convolutional network to improve SBR. Moreover,
to enhance hypergraph modeling, we devise another graph convolutional network
which is based on the line graph of the hypergraph and then integrate
self-supervised learning into the training of the networks by maximizing mutual
information between the session representations learned via the two networks,
serving as an auxiliary task to improve the recommendation task. Since the two
types of networks both are based on hypergraph, which can be seen as two
channels for hypergraph modeling, we name our model \textbf{DHCN} (Dual Channel
Hypergraph Convolutional Networks). Extensive experiments on three benchmark
datasets demonstrate the superiority of our model over the SOTA methods, and
the results validate the effectiveness of hypergraph modeling and
self-supervised task. The implementation of our model is available at
https://github.com/xiaxin1998/DHCNComment: 9 pages, 4 figures, accepted by AAAI'2
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