Neural Attentive Session-based Recommendation

@article{Li2017NeuralAS,
  title={Neural Attentive Session-based Recommendation},
  author={J. Li and Pengjie Ren and Zhumin Chen and Z. Ren and Tao Lian and J. Ma},
  journal={Proceedings of the 2017 ACM on Conference on Information and Knowledge Management},
  year={2017}
}
  • J. Li, Pengjie Ren, +3 authors J. Ma
  • Published 2017
  • Computer Science
  • Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. [...] Key Method Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later.Expand
Personalizing Session-based Recommendation with Dual Attentive Neural Network
TLDR
This work designs a novel neural network framework for personalized session-based recommendation, named Dual Attentive Neural Network (DANN), which considers user’s main purpose of current session and users’ personalized preference of cross-session and achieves improvement when modeling user personalized preferences. Expand
Leveraging neighborhood session information with dual attentive neural network for session-based recommendation
TLDR
This study proposes a novel Leveraging Neighborhood Session Information with Dual Attentive Neural Network (LNIDA) for session-based recommendation and finds out that LNIDA can improve performance when modeling the current session information and the neighborhood session information simultaneously. Expand
Learning sequential and general interests via a joint neural model for session-based recommendation
TLDR
This paper proposes a joint neural model for jointly learning sequential and general interests of each session for session-based recommendation, called SGINM, which achieves better recommendation performance in terms of higher predicative accuracy and mean reciprocal rank. Expand
Dual Sparse Attention Network For Session-based Recommendation
TLDR
Experimental results on two real public datasets show that the proposed DSAN method is superior to the state-of-the-art sessionbased recommendation algorithm in all tests and also demonstrate that not all actions within the session are useful. Expand
A Neighbor-Guided Memory-Based Neural Network for Session-Aware Recommendation
TLDR
A neighbor-guided memory-based neural network (MNN) is proposed for session-aware recommendation task, which comprehensively considers users’ short-term intent, long-term preference and cross-sessions information to yield the final recommendations. Expand
Session-Based Recommendation with Self-Attention
TLDR
A neural network architecture for session-based recommendation without using convolution or recurrent neural networks is proposed, inspired by the Transformer's design, in which the information of important items is passed directly to the hidden states. Expand
Session-based Recommendation with Context-Aware Attention Network
TLDR
A novel method, i.e., Session-based Recommendation with Context-Aware Attention Network, SR-CAAN is proposed, which enhances the ability of modeling the user preference by combining sequence prediction with session external context aware method. Expand
Session-based Recommendation with Graph Neural Networks
TLDR
In the proposed method, session sequences are modeled as graph-structured data and GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Expand
Spatio-Temporal Attentive Network for Session-Based Recommendation
TLDR
A hybrid framework based on Graph Neural Network and Gated Recurrent Unit is designed to obtain richer item representations from spatio-temporal perspective and an individual-level skipping strategy, which considers the randomness of user’s behaviors, is proposed to enrich item representations. Expand
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
TLDR
This paper designs a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system and proposes a weighted attention graph layer and a Readout function to learn embeddings of items and sessions for the next item recommendation. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 47 REFERENCES
Session-based Recommendations with Recurrent Neural Networks
TLDR
It is argued that by modeling the whole session, more accurate recommendations can be provided by an RNN-based approach for session-based recommendations, and introduced several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Expand
A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
TLDR
This work proposes a content-based recommendation system to address both the recommendation quality and the system scalability, and proposes to use a rich feature set to represent users, according to their web browsing history and search queries, using a Deep Learning approach. Expand
Improved Recurrent Neural Networks for Session-based Recommendations
TLDR
This work proposes the application of two techniques to improve RNN-based models for session-based recommendations performance, namely, data augmentation, and a method to account for shifts in the input data distribution. Expand
Learning Hierarchical Representation Model for NextBasket Recommendation
TLDR
This paper introduces a novel recommendation approach, namely hierarchical representation model (HRM), which can well capture both sequential behavior and users' general taste by involving transaction and user representations in prediction. Expand
Neural Rating Regression with Abstractive Tips Generation for Recommendation
TLDR
A deep learning based framework named NRT is proposed which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings. Expand
Neural Collaborative Filtering
TLDR
This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering. Expand
Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
TLDR
It is demonstrated that the proposed model is a generalization of several well-known collaborative filtering models but with more flexible components, and that CDAE consistently outperforms state-of-the-art top-N recommendation methods on a variety of common evaluation metrics. Expand
Social Collaborative Viewpoint Regression with Explainable Recommendations
TLDR
This paper proposes a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations, and uses so-called viewpoints, represented as tuples of a concept, topic, and a sentiment label from both user reviews and trusted social relations. Expand
Enlister: baidu's recommender system for the biggest chinese Q&A website
In this paper, we describe the concept & design of a real-time question RS (recommender system), the Enlister project, for the biggest Chinese Q&A (Questions and Answers) website and evaluate itsExpand
BPR: Bayesian Personalized Ranking from Implicit Feedback
TLDR
This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt. Expand
...
1
2
3
4
5
...