Session-based Recommendation Methods Research Articles

Gated graph spiking neural P network for session-based recommendation

Session-based recommendation (SBR) is a recommendation system application scenario that focuses on user interests and behaviors in sessions formed within a short period. However, existing SBR methods do not fully extract user contextual information, and gated graph neural networks(GNNs) still face challenges in handling long-term dependencies. The nonlinear spiking neural P system is a novel spiking neural-like computing model in which the nonlinear spiking mechanism is a typical feature. Based on this nonlinear spiking mechanism, we propose a new recurrent-like model called the extended gated spiking neural P model or the EGSNP model, which can effectively capture temporal relationships in their sequence and obtain the user’s contextual information to alleviate information extraction problems in SBR. Based on this EGSNP model, we propose a new type of GNN called the extended gated graph spiking neural P network or termed the EGGSNP network. Finally, we use the EGSNP model and EGGSNP network to construct a new SBR model for the session recommendation task, termed the SR-EGGSNP model. This SR-EGGSNP model can effectively extract contextual information and flexibly capture the long-term evolution of interest while alleviating the long-distance dependency problem in GNNs. We conducted extensive comparative experiments on two public datasets, and the results demonstrated the effectiveness of the proposed model and accuracy of the recommendations.

SR-MACL: Session-Based Recommendation with Multi-layer Aggregation Augmentation in Contrastive Learning

Session-based recommendation (SBR) aims to predict the next item of interest in chronological order based on a given sequence of short-term behaviour of anonymous users. Due to the limited data available for short-term user interactions, its performance is more susceptible to data sparsity problems than traditional recommendation methods. Contrastive learning is often used to solve the data sparsity problem due to its ability to extract general features from the raw data. Existing session-based recommendation methods based on graph contrastive learning typically build graph contrastive learning by using information from other sessions to generate augmented views. While this avoids the problem that the use of dropout in traditional contrast learning methods can cause damage to the session context, it inevitably introduces irrelevant item information, which interferes with accurately modelling user interests and leads to sub-optimal model performance. To address these issues, we propose a new session recommendation method based on multi-layer aggregation augmentation contrastive learning, namely SR-MACL. In SR-MACL we construct a contrastive view by adding noise to the embedding representation and forming a contrastive embedding representation by multi-layer aggregation, which not only effectively solves the problem that traditional graph enhancement methods can destroy the context of the whole session, but also avoids the interference of irrelevant items. Experimental results on three real datasets have shown that SR-MACL can improve the accuracy of recommendation results and predict the user's next interaction more effectively.

GTPAN: Global Target Preference Attention Network for session-based recommendation

Session-based recommendations (SBR) aim to predict the user’s next choice of items based on historical data. It is hard to access the interaction information between users and we can only analyze the user’s behavioral preferences based on the current session characteristics, which makes the recommendation task in this field quite challenging. Recently, considerable research has grown up around the theme of SBR. Previous works apply attention mechanisms and Graph Neural Networks (GNN) to achieve remarkable results. However, these methods ignore two main points: one is losing sight of the specific position of items in the session; the other is to drive global preference with local preference without considering the offset of the target preference among items in the session. This paper proposes a Global Target Preference Attention Network (GTPAN) Model with position information for the session-based recommendation. We introduce a position-aware embedding module to eliminate the effect of position missing. Considering that each item in the session is potentially rich in latent information of target preference, we use a target preference attention layer to learn the target preference vector of the session. Extensive experiments on three real datasets show that the proposed GTPAN model outperforms state-of-the-art session-based recommendation methods, especially on datasets with long sessions.

Combine temporal information in session-based recommendation with graph neural networks

With the rapid growth of Internet data, recommendation systems have become the basic technology to alleviate information overload. The session-based recommendation (SBR) is a challenging task, and its purpose is to predict user behavior based on anonymous sessions. The existing SBR methods need to be improved in capturing the complex conversion relationship of items. In contrast, Graph Neural Network (GNN) can capture the complex conversion between items by modeling sessions as graph structure data. However, current methods just sort the clicked items within a session based on time, without utilizing the temporal information between sessions, leading to poor recommendation performance. To improve the accuracy of session recommendation (SR), we propose an SBR model based on GNN with Combined Temporal (CT-GNN) information. The proposed CT-GNN model is built based on the time of session occurrence, and it can learn the temporal association relationship between session items to enrich the connection between items. More importantly, based on the diversity problem faced by current SRs, which refers to the tendency of users to engage with popular items and resulting in limited exposure for other items, the CT-GNN model uses the Local Item Representation Learning (LIRL) module to learn users' local preferences. Through the LIRL module, the CT-GNN can capture users' interests, boost item exposure, and increase recommendation diversity. The experimental results show that the CT-GNN model is superior to the state-of-the-art SBR methods, with MRR scores 1.1 %∼3.4 % higher than the best-performing baseline; recall scores 0.9 %∼2.3 % higher than the best-performing baseline, and alleviates the diversity problem faced by SR, greatly improving the speed of model convergence, and the training time is 13.6 %∼20.7 % of the best-performing baseline.

Improve Session-Based Recommendation with Triplet Mining and Dynamic Perturbations Graph Neural Networks

Session-based recommendation (SBR) emphasizes mining user interests to predict the next click based on recent interactions within sessions. Most current SBR methods suffer from insufficient interactive information problems and fail to distinguish session representations with high similarities, which can neglect the inherent features within sessions. To fill the gap, we propose a triplet mining enhanced graph neural networks (TME-GNN) approach to enhance the recommendation systems by mining structural and inherent information. Technically, we first generate anchor, positive and negative embeddings based on the given session and set a triplet mining task to improve the recommendation task with subtle features by pushing positive pairs close and pulling negative pairs away. Second, to robust the model, we employ a self-supervised auxiliary task by adding dynamic perturbations to the embedding space. We conduct extensive experiments to demonstrate the superiority of our method against other state-of-the-art algorithms. Our implementations are available on the following site https://github.com/Info4Rec/TME-GNN .

Cross-view temporal graph contrastive learning for session-based recommendation

Session-based recommendation (SBR) aims at recommending items given the behavior sequences of anonymous users in a short-term session. Many recent SBR methods construct all sessions as a global graph that captures cross-session item transition patterns (i.e., users’ global preference) to alleviate the problem of session data sparsity. However, these methods neglect that users’ interests will drift as the time between the sessions constantly increases, limiting the performance improvement of SBR. To fill this gap, we divide all sessions into a group of time-slices and model the cross-session item transitions for every time-slice. We further construct two augmentation views (i.e., temporal graph and temporal hypergraph views) to model pairwise and high-order item transitions on SBR. In addition, we construct contrastive learning between two views to improve the recommendation performance by maximizing the mutual information between the item representations learned from the two views. Experiments on three public real-world datasets (i.e., Diginetica, Retailrocket, and Yoochoose) show that our model is consistently superior to the other state-of-the-art baselines, especially in time-sensitive datasets. For instance, our model achieves 14.42% and 11.72% improvements in terms of P@10 and P@20 on the Diginetica dataset, respectively.

Session-Based Recommendations for e-Commerce with Graph-Based Data Modeling

Conventional recommendation methods such as collaborative filtering cannot be applied when long-term user models are not available. In this paper, we propose two session-based recommendation methods for anonymous browsing in a generic e-commerce framework. We represent the data using a graph where items are connected to sessions and to each other based on the order of appearance or their co-occurrence. In the first approach, called Hierarchical Sequence Probability (HSP), recommendations are produced using the probabilities of items’ appearances on certain structures in the graph. Specifically, given a current item during a session, to create a list of recommended next items, we first compute the probabilities of all possible sequential triplets ending in each candidate’s next item, then of all candidate item pairs, and finally of the proposed item. In our second method, called Recurrent Item Co-occurrence (RIC), we generate the recommendation list based on a weighted score produced by a linear recurrent mechanism using the co-occurrence probabilities between the current item and all items. We compared our approaches with three state-of-the-art Graph Neural Network (GNN) models using four session-based datasets one of which contains data collected by us from a leather apparel e-shop. In terms of recommendation effectiveness, our methods compete favorably on a number of datasets while the time to generate the graph and produce the recommendations is significantly lower.

Dynamic global structure enhanced multi-channel graph neural network for session-based recommendation

Session-based recommendation is a challenging task, which aims at making recommendation for anonymous users based on in-session data, i.e. short-term interaction data. Most session-based recommendation methods only model user’s preferences with the current session sequence, which ignore rich information from a global perspective. Meanwhile, previous works usually apply GNN to capture the transformation relationship between items, however the graph used in GNN is built through a static mode, which may introduce noise to the graph structure if user’s preferences shift. In this paper, we propose a novel method called Dynamic Global Structure Enhanced Multi-channel Graph Neural Network (DGS-MGNN) to learn accurate representations of items from multiple perspectives. In DGS-MGNN, we propose a novel GNN model named Multi-channel Graph Neural Network to generate the local, global and consensus graphs dynamically and learn more informative representations of items based on the corresponding graph. Meanwhile, in order to reduce the noise information within sessions, we utilize the graph structure to assist the attention mechanism to filter noisy information within each session, so as to generate an accurate intention representation for the user. Finally, combined with a repeat and explore module, a more accurate prediction probability distribution is generated. We conduct extensive experiments on three widely used datasets, and the results demonstrate that DGS-MGNN is consistently superior to the state-of-the-art baseline models.

Session-based recommendation with time-aware neural attention network

In situations where user information and detailed knowledge of user behaviors are challenging to obtain, the session-based recommendation is essential. The session-based recommendation (SBR) relies on the current anonymous session sequence to predict the next action. Recently, sequence modeling methods such as recurrent neural networks have been widely used to model user preferences in session-based recommendations. However, the existing SBR methods usually ignore the click time of interacted items, resulting in inaccurate user preference models and poor recommendations. In this article, we propose a time-aware neural attention network to model dynamic user preferences in SBR tasks. First, a global session graph is constructed based on all sessions, and a graph neural network is used to learn the embedding of items. Then, a gated recurrent unit is used to refine the item embedding and capture the general interests of the user based on the current session. Next, a novel time-aware neural attention network is proposed and used to model the main purpose of the user in the session. Finally, the user’s general interests and main purposes are combined to obtain dynamic user preferences for generating recommendations. Extensive experiments show that our method outperforms state-of-the-art session-based recommendation methods significantly and consistently.

Hybrid-Order Gated Graph Neural Network for Session-Based Recommendation

Considering sessions as directed subgraphs, graph neural networks (GNNs) are supposed to be capable of capturing the complex dependencies among items and suitable for session-based recommendation. However, deep GNNs suffer from the oversmoothing problem of making all nodes converge to the same value. In session-based recommendation, the subgraphs transformed by short sessions are usually simple, which cause worse oversmoothing problem. To apply GNNs to session-based recommendation sufficiently, in this article, we propose a hybrid-order gated GNN (HGNN) on account of the oversmoothing problem. The proposed HGNN model is based on the hybrid-order propagation, which avoids insignificant patterns and captures complex dependencies in propagation. What's more, the attention mechanism is utilized to learn different weights of orders in propagation. Then, HGNN is applied to session-based recommendation, which results in a new method called SR-HGNN. Experimental results show that SR-HGNN outperforms the state-of-the-art session-based recommendation methods and eases the oversmoothing problem.

Graph hierarchical dwell-time attention network for session-based recommendation

Session-based recommendation (SBR) is making item recommendations based on anonymous click behavior. SBR based on graph neural networks has shown great power in recent years. It can enhance the representation of items in a session. The aggregation of items is then used to generate a session vector for the recommendation. However, existing SBR models rarely consider the impact of dwell-time in session data when performing session item aggregation. The dwell-time contains the implicit behavior of anonymous users in the session sequence. In order to obtain a more accurate session embedding and take into account the impact of multiple perspectives, we propose a new model, graph hierarchical dwell-time attention network. This approach uses a modified graph neural network to learn session items by extracting loss information from graph modeling. We also design a hierarchical dwell-time attention module that uses the effect of dwell-time to generate long-term preferences for sessions. Experimental results show that GHDAN outperforms the state-of-the-art session-based recommendation methods.

Personal Interest Attention Graph Neural Networks for Session-Based Recommendation.

Session-based recommendations aim to predict a user’s next click based on the user’s current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user’s interest preferences at the current moment, thus limiting the accuracy of recommendations. Considering the diversity of items and users’ interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users’ interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users’ long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods.

DUAL TRANSFORMER ENCODERS FOR SESSION-BASED RECOMMENDATION

When long-term user proles are not available, session-based recommendation methods are used to predict the user's next actions from anonymous sessions-based data. Recent advances in session-based recommendation highlight the necessity of modeling not only user sequential behaviors but also the user's main interest in a session, while avoiding the eect of unintended clicks causing interest drift of the user. In this work, we propose a Dual Transformer Encoder Recommendation model (DTER) as a solution to address this requirement. The idea is to combine the following recipes: (1) a Transformer-based model with dual encoders capable of modeling both sequential patterns and the main interest of the user in a session; (2) a new recommendation model that is designed for learning richer session contexts by conditioning on all permutations of the session prex. This approach provides a unied framework for leveraging the ability of the Transformer's self-attention mechanism in modeling session sequences while taking into account the user's main interest in the session. We empirically evaluate the proposed method on two benchmark datasets. The results show that DTER outperforms state-of-the-art session-based recommendation methods on common evaluation metrics.

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 dual channel hypergraph convolutional network -- DHCN to improve SBR. Moreover, to enhance hypergraph modeling, we innovatively integrate self-supervised learning into the training of our network by maximizing mutual information between the session representations learned via the two channels in DHCN, serving as an auxiliary task to improve the recommendation task. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over the SOTA methods, and the ablation study validates the effectiveness and rationale of hypergraph modeling and self-supervised task. The implementation of our model is available via https://github.com/xiaxin1998/DHCN.

An Academic Text Recommendation Method Based on Graph Neural Network

Academic text recommendation, as a kind of text recommendation, has a wide range of application prospects. Predicting texts of interest to scholars in different fields based on anonymous sessions is a challenging problem. However, the existing session-based method only considers the sequential information, and pays more attention to capture the session purpose. The relationship between adjacent items in the session is not noticed. Specifically in the field of session-based text recommendation, the most important semantic relationship of text is not fully utilized. Based on the graph neural network and attention mechanism, this paper proposes a session-based text recommendation model (TXT-SR) incorporating the semantic relations, which is applied to the academic field. TXT-SR makes full use of the tightness of semantic connections between adjacent texts. We have conducted experiments on two real-life academic datasets from CiteULike. Experimental results show that TXT-SR has better effectiveness than existing session-based recommendation methods.

Incorporating Link Prediction into Multi-Relational Item Graph Modeling for Session-based Recommendation

Session-based recommendation aims at predicting the next item that a user is more likely to interact with by a target behavior type. Most of the existing session-based recommendation methods focus on developing powerful representation learning approaches to model items' sequential correlations, whereas they usually encounter the following limitations. Firstly, they only utilize sessions that belong to the target behavior type, neglecting the potential of leveraging other behavior types as auxiliary information for modeling user preference. Secondly, they separately model item-to-item relations for each session, overlooking to globally characterize the relations across different sessions for better item representations. To overcome these limitations, we first build a Multi-Relational Item Graph (MRIG) involving target and auxiliary behavior types over all sessions. Consequently, a novel Graph Neural Network (GNN) based model is devised to encode MRIG's item-to-item relations into target and auxiliary session-based representations, and adaptively fuse them to represent user interests. To facilitate model training, we further incorporate link prediction into multi-relational item graph modeling, acting as a simple but relevant task to session-based recommendation. The extensive experiments on real-world datasets demonstrate the superiority of the model over diverse and competitive baselines, validating its main components' significant contributions.

Recurrent convolutional networks for session-based recommendations

Traditional recommender systems help users find items of interest by modeling long-term user profiles, which consist of items the users interacted with in the past. In many real-life applications, however, informative user profiles are not available. Instead, the system recommends by relying on the current user activities within an ongoing session, leading to the emergence of session-based recommendation methods. Among techniques used in such situations, recurrent neural networks (RNNs) present a natural choice thanks to their ability to model the order of session events and capture long-term dependencies. Recently, methods based on convolutional neural networks (CNNs) have also shown their potential in modeling session data, especially in extracting complex local patterns that are predictive of the user target. In this work, we propose a recurrent convolutional architecture that takes the advantages of both complex local features extracted by CNNs and long-term dependencies learned by RNNs from session sequences. Our model has two main layers: the lower layer consists of convolutional filters applied over consecutive session event embeddings, and the upper layer is a gated recurrent unit (GRU) RNN that takes as input the CNN’s output. This hybrid approach provides a flexible and unified network architecture for modeling various important features of session sequences. Experiments conducted on three benchmark datasets demonstrate the superiority of the proposed model over pure RNNs and CNNs models as well as state-of-the-art session-based recommendation methods.

WGIN: A Session-Based Recommendation Model Considering the Repeated Link Effect

Session-based recommendation systems have high application value. Determining how to make better use of anonymous user sessions to recommend items of interest is a considerable challenge for current recommendation systems. Existing research has mainly focused on sequential session patterns; however, due to the complexity and diversity of user interests, such interests cannot be effectively modeled in this way. Therefore, in this paper, we investigate the transition patterns between items by constructing a session graph and propose a novel model called Weighted Graph Interest Networks (WGIN) that collaboratively considers hidden user preference information and the potential order of items in the session graph for a session-based recommendation system. Specifically, we propose a repetitive weighted graph neural network (RWGNN), which pays attention to the transitions between frequent items in a session to deeply explore the preferences of users. In addition, we establish a new Transformer structure to model long-term and short-term user preferences and obtain rich session embeddings. Extensive experiments on two real datasets illustrate that the proposed model outperforms other state-of-the-art session-based recommendation methods.

Session-Based Recommendation with Graph Neural Networks

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.

A Session-Based Customer Preference Learning Method by Using the Gated Recurrent Units With Attention Function

In this paper, we investigate an attention function combined with the gated recurrent units (GRUs), named GRUA, to raise the accuracy of the customer preference prediction. The attention function extracts the important product features by using the time-bias parameter and the term frequency-inverse document frequency parameter for recommending products to a customer in the ongoing session. We show that the attention function with the GRUs can learn the customer’s intention in the ongoing session more precisely than the existing session-based recommendation (SBR) methods. The experimental results show that the GRUA outperforms two SBR methods: the stacked denoising autoencoders with collaborative filtering (SDAE/CF) and the GRUs with collaborative filtering (GRU/CF) based on the precision and recall evaluation metrics. The data from three publicly available datasets, the Amazon Product Review dataset, the Xing dataset, and the Yoo-Choose Click dataset, are used to evaluate the performance of the GRUA with the SDAE/CF and the GRU/CF. This paper shows that adopting the attention function into the GRUs can dramatically increase the accuracy of the product recommendation in the SBR.