233,096 research outputs found
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning
Session-based recommendation techniques aim to capture dynamic user behavior
by analyzing past interactions. However, existing methods heavily rely on
historical item ID sequences to extract user preferences, leading to challenges
such as popular bias and cold-start problems. In this paper, we propose a
hybrid multimodal approach for session-based recommendation to address these
challenges. Our approach combines different modalities, including textual
content and item IDs, leveraging the complementary nature of these modalities
using CatBoost. To learn universal item representations, we design a language
representation-based item retrieval architecture that extracts features from
the textual content utilizing pre-trained language models. Furthermore, we
introduce a novel Decoupled Contrastive Learning method to enhance the
effectiveness of the language representation. This technique decouples the
sequence representation and item representation space, facilitating
bidirectional alignment through dual-queue contrastive learning.
Simultaneously, the momentum queue provides a large number of negative samples,
effectively enhancing the effectiveness of contrastive learning. Our approach
yielded competitive results, securing a 5th place ranking in KDD CUP 2023 Task
1. We have released the source code and pre-trained models associated with this
work
Self-Supervised Multi-Modal Sequential Recommendation
With the increasing development of e-commerce and online services,
personalized recommendation systems have become crucial for enhancing user
satisfaction and driving business revenue. Traditional sequential
recommendation methods that rely on explicit item IDs encounter challenges in
handling item cold start and domain transfer problems. Recent approaches have
attempted to use modal features associated with items as a replacement for item
IDs, enabling the transfer of learned knowledge across different datasets.
However, these methods typically calculate the correlation between the model's
output and item embeddings, which may suffer from inconsistencies between
high-level feature vectors and low-level feature embeddings, thereby hindering
further model learning. To address this issue, we propose a dual-tower
retrieval architecture for sequence recommendation. In this architecture, the
predicted embedding from the user encoder is used to retrieve the generated
embedding from the item encoder, thereby alleviating the issue of inconsistent
feature levels. Moreover, in order to further improve the retrieval performance
of the model, we also propose a self-supervised multi-modal pretraining method
inspired by the consistency property of contrastive learning. This pretraining
method enables the model to align various feature combinations of items,
thereby effectively generalizing to diverse datasets with different item
features. We evaluate the proposed method on five publicly available datasets
and conduct extensive experiments. The results demonstrate significant
performance improvement of our method
Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics
Mouse dynamics is a potential means of authenticating users. Typically, the
authentication process is based on classical machine learning techniques, but
recently, deep learning techniques have been introduced for this purpose.
Although prior research has demonstrated how machine learning and deep learning
algorithms can be bypassed by carefully crafted adversarial samples, there has
been very little research performed on the topic of behavioural biometrics in
the adversarial domain. In an attempt to address this gap, we built a set of
attacks, which are applications of several generative approaches, to construct
adversarial mouse trajectories that bypass authentication models. These
generated mouse sequences will serve as the adversarial samples in the context
of our experiments. We also present an analysis of the attack approaches we
explored, explaining their limitations. In contrast to previous work, we
consider the attacks in a more realistic and challenging setting in which an
attacker has access to recorded user data but does not have access to the
authentication model or its outputs. We explore three different attack
strategies: 1) statistics-based, 2) imitation-based, and 3) surrogate-based; we
show that they are able to evade the functionality of the authentication
models, thereby impacting their robustness adversely. We show that
imitation-based attacks often perform better than surrogate-based attacks,
unless, however, the attacker can guess the architecture of the authentication
model. In such cases, we propose a potential detection mechanism against
surrogate-based attacks.Comment: Accepted in 2019 International Joint Conference on Neural Networks
(IJCNN). Update of DO
Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation
Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches
Hybrid modeling, HMM/NN architectures, and protein applications
We describe a hybrid modeling approach where the parameters of a model are calculated and modulated by another model, typically a neural network (NN), to avoid both overfitting and underfitting. We develop the approach for the case of Hidden Markov Models (HMMs), by deriving a class of hybrid HMM/NN architectures. These architectures can be trained with unified algorithms that blend HMM dynamic programming with NN backpropagation. In the case of complex data, mixtures of HMMs or modulated HMMs must be used. NNs can then be applied both to the parameters of each single HMM, and to the switching or modulation of the models, as a function of input or context. Hybrid HMM/NN architectures provide a flexible NN parameterization for the control of model structure and complexity. At the same time, they can capture distributions that, in practice, are inaccessible to single HMMs. The HMM/NN hybrid approach is tested, in its simplest form, by constructing a model of the immunoglobulin protein family. A hybrid model is trained, and a multiple alignment derived, with less than a fourth of the number of parameters used with previous single HMMs
Transfer Learning for Neural Semantic Parsing
The goal of semantic parsing is to map natural language to a machine
interpretable meaning representation language (MRL). One of the constraints
that limits full exploration of deep learning technologies for semantic parsing
is the lack of sufficient annotation training data. In this paper, we propose
using sequence-to-sequence in a multi-task setup for semantic parsing with a
focus on transfer learning. We explore three multi-task architectures for
sequence-to-sequence modeling and compare their performance with an
independently trained model. Our experiments show that the multi-task setup
aids transfer learning from an auxiliary task with large labeled data to a
target task with smaller labeled data. We see absolute accuracy gains ranging
from 1.0% to 4.4% in our in- house data set, and we also see good gains ranging
from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and
semantic auxiliary tasks.Comment: Accepted for ACL Repl4NLP 201
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