Neural Attentive Session-based Recommendation

By Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, Jun Ma. International Conference on Information and Knowledge Management, 2017

1,622 citations recorded by the source corpus.

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. 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. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/97de32d8ca162944e5f7e83071c596d13d168109

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Wayne Xin Zhao
3. Xiang Wang
4. Tat-seng Chua
5. Ji-rong Wen
6. Hongzhi Yin
7. Meng Wang
8. Yi Tay

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Wayne Xin Zhao
4. Hongzhi Yin
5. Tat-seng Chua
6. Meng Wang
7. Ji-rong Wen
8. Le Wu

In Management Science and Operations Research

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Meng Wang
5. Julian McAuley
6. Le Wu
7. Jiacheng Li
8. Xue Liu

Citations in the corpus

• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (2019, methodology): “The basic idea of these methods is to encode user’s previous records into a vector (i.e., representation of user’s preference which is used to make predictions) with various recurrent architectures and loss functions, including session-based GRU with ranking loss (GRU4Rec) [15], Dynamic REcurrent bA”
• Hierarchical Gating Networks for Sequential Recommendation (2019, methodology): “s. For instance, Tang et al. [34] propose to apply the convolutional neural network (CNN) on item embedding sequence, where the short-term contexts can be captured by the convolutional operations. In [12, 13, 20, 28], recurrent neural network (RNN), especially gated recurrent unit (GRU), based metho”
• NAIS: Neural Attentive Item Similarity Model for Recommendation (2018, methodology): “design of neural attention network [15], [16], [17], [19] did not work well for item-based CF, due to the large variances of the lengths of user histories.”
• Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation (2023, methodology): “In existing literature, sequential recommenders adopt various deep neural networks to model user historical behaviors represented by item ID sequences, including RNN [1], [3], Corresponding author.”
• Global Context Enhanced Graph Neural Networks for Session-based Recommendation (2020, methodology): “Recently, many deep learning based approaches are proposed for the task, which make use of pairwise item-transition information to model the user preference of a given session [2, 4, 6, 18, 19, 21].”
• Text Is All You Need: Learning Language Representations for Sequential Recommendation (2023, methodology): “Various methods have been proposed to improve the performance of sequential recommendation, including Markov Chains [9, 25], RNN/CNN models [11, 17, 28, 34] and self-attentive models [14, 19, 27].”
• LARA: Attribute-to-feature Adversarial Learning for New-item Recommendation (2020, background): “Personalized recommendation has become an increasingly vital component in many online applications, such as e-commerce portals and content sharing applications [20, 28].”
• Artificial intelligence in recommender systems (2020, background): “They are equally integrated and matched with an item through a bi-linear scheme [99].”
• RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020, neutral): “NARM [35] CIKM 2017 YOOCHOOSE, DIGINETICA”
• A Simple Convolutional Generative Network for Next Item Recommendation (2018, methodology): “is paper do not focus on combining various features, we omit the comparison with content- or context-based sequential recommendation models, such as the 3D CNN recommender [28] and other RNN variants [10, 18, 22, 25]; (2) the GRURec baseline could be regarded as the state-of-the-art Improved GRURec ”
• Session-based Recommendation with Graph Neural Networks (2018, methodology): “Compared with the state-of-art methods like NARM and STAMP, SR-GNN further considers transitions between items in a session and thereby models every session as a graph, which can capture more complex and implicit connections between user clicks.”
• Deep Session Interest Network for Click-Through Rate Prediction (2019, background): “Based on that, [ Li et al. , 2017 ] proposes a novel attentive neural networks framework (NARM) to model the user’s sequential behavior and capture the user’s main purpose in the current session.”
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020, methodology): “For both datasets, we follow (Wu et al. 2019b; Li et al. 2017) to remove all sessions containing only one item and also remove items appearing less than five times.”
• Recommender Systems with Generative Retrieval (2023, methodology): “On the other hand, sequential recommenders [6, 11, 17, 23, 31, 46, 49], which explicitly take into account the order of user-item interactions, are also recently popular.”
• Contrastive Learning for Sequential Recommendation (2022, methodology): “The following variants modified GRU4Rec by introducing attention mechanism [4, 29, 30], hierarchical structure [36], and user-based gated network [11].”
• Session-Based Social Recommendation via Dynamic Graph Attention Networks (2019, background): “Preliminary results showed that our method consistently outperformed SessionRNN and NARM for other session lengths.”
• STAN: Spatio-Temporal Attention Network for Next Location Recommendation (2021, background): “They have achieved satisfactory performances on variety of tasks, such as session-based recommendation [11, 16], next basket recommendation [37] and next item recommendation [1, 44].”
• ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation (2020, methodology): “Despite the recent success of various neural approaches [11, 16, 18, 20], they are developed in an offline manner, in which the recommender is trained on a very large static training set and evaluated on a very restrictive testing set in a one-time process.”