40,451 research outputs found
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
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Recommendations can greatly benefit from good representations of the user
state at recommendation time. Recent approaches that leverage Recurrent Neural
Networks (RNNs) for session-based recommendations have shown that Deep Learning
models can provide useful user representations for recommendation. However,
current RNN modeling approaches summarize the user state by only taking into
account the sequence of items that the user has interacted with in the past,
without taking into account other essential types of context information such
as the associated types of user-item interactions, the time gaps between events
and the time of day for each interaction. To address this, we propose a new
class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that
can take into account the contextual information both in the input and output
layers and modifying the behavior of the RNN by combining the context embedding
with the item embedding and more explicitly, in the model dynamics, by
parametrizing the hidden unit transitions as a function of context information.
We compare our CRNNs approach with RNNs and non-sequential baselines and show
good improvements on the next event prediction task
A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation
E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customer’s need is crucial for retailers to increase revenue and retain customers’ loyalty. As users’ interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand users’ long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product recommendation as an application and classifies SRecSys under three main categories as: (i) traditional approaches (sequence similarity, frequent pattern mining and sequential pattern mining), (ii) factorization and latent representation (matrix factorization and Markov models) and (iii) neural network-based approaches (deep neural networks, advanced models). This classification contributes towards enhancing the understanding of existing SRecSys in the literature with the application domain of e-commerce product recommendation and provides current status of the solutions available alongwith future research directions. Furthermore, a classification of surveyed systems according to eight important key features supported by the techniques along with their limitations is also presented. A comparative performance analysis of the presented SRecSys based on experiments performed on e-commerce data sets (Amazon and Online Retail) showed that integrating sequential purchase patterns into the recommendation process and modeling users’ sequential behavior improves the quality of recommendations
Dynamic Graph Attention-Aware Networks for Session-Based Recommendation
Graph convolutional neural networks have attracted increasing attention in recommendation system fields because of their ability to represent the interactive relations between users and items. At present, there are many session-based methods based on graph neural networks. For example, SR-GNN establishes a user’s session graph based on the user’s sequential behavior to predict the user’s next click. Although these session-based recommendation methods modeling the user’s interaction with items as a graph, these methods have achieved good performance in improving the accuracy of the recommendation. However, most existing models ignore the items’ relationship among sessions. To efficiently learn the deep connections between graph-structured items, we devised a dynamic attention-aware network (DYAGNN) to model the user’s potential behavior sequence for the recommendation. Extensive experiments have been conducted on two real-world datasets, the experimental results demonstrate that our method achieves good results in capturing user attention perception
Topic-enhanced memory networks for personalised point-of-interest recommendation
Point-of-Interest (POI) recommender systems play a vital role in people's
lives by recommending unexplored POIs to users and have drawn extensive
attention from both academia and industry. Despite their value, however, they
still suffer from the challenges of capturing complicated user preferences and
fine-grained user-POI relationship for spatio-temporal sensitive POI
recommendation. Existing recommendation algorithms, including both shallow and
deep approaches, usually embed the visiting records of a user into a single
latent vector to model user preferences: this has limited power of
representation and interpretability. In this paper, we propose a novel
topic-enhanced memory network (TEMN), a deep architecture to integrate the
topic model and memory network capitalising on the strengths of both the global
structure of latent patterns and local neighbourhood-based features in a
nonlinear fashion. We further incorporate a geographical module to exploit
user-specific spatial preference and POI-specific spatial influence to enhance
recommendations. The proposed unified hybrid model is widely applicable to
various POI recommendation scenarios. Extensive experiments on real-world
WeChat datasets demonstrate its effectiveness (improvement ratio of 3.25% and
29.95% for context-aware and sequential recommendation, respectively). Also,
qualitative analysis of the attention weights and topic modeling provides
insight into the model's recommendation process and results.China Scholarship Council and Cambridge Trus
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