Session-based Recommendation Research Articles

Leveraging User Session for Personalized E- Commerce Recommendation

The advent of the internet has propelled many shopping activities online, leading to the rapid growth of e-commerce. This shift has revolutionized the shopping experience, offering unparalleled convenience with anytime, anywhere access via computers and internet connectivity. Moreover, the vast array of easily accessible choices empowers buyers to make well-informed decisions. Numerous websites have emerged to provide e-commerce services, catering either as a complement to physical stores or as standalone businesses. However, the abundance of offerings often leads to information overload for buyers, making product searches time-consuming and frustrating. Personalized e-commerce recommendations alleviate this challenge by guiding users to relevant products swiftly, enhancing the overall shopping experience and ultimately boosting product sales. The study focuses on creating a session-based recommendation system for e-commerce websites, leveraging Recurrent Neural Networks with LSTM architectures to analyze sequential user behavior and browsing context for personalized product recommendations. The research methodology encompasses data collection and preprocessing, where data was splitted into training, testing and validation set. The model was efficiency was evaluated using precision, recall and mean reciprocal rank with the result showing considerable promise for recommendation. This research makes a substantial contribution by suggesting tailored options, users are more likely to find suitable products, leading to increased satisfaction and repeat purchases, thereby benefiting e-commerce platforms.

Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation

Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation

H3GNN: Hybrid Hierarchical HyperGraph Neural Network for Personalized Session-based Recommendation

Personalized Session-based recommendation (PSBR) is a general and challenging task in the real world, aiming to recommend a session’s next clicked item based on the session’s item transition information and the corresponding user’s historical sessions. A session is defined as a sequence of interacted items during a short period. The PSBR problem has a natural hierarchical architecture in which each session consists of a series of items, and each user owns a series of sessions. However, the existing PSBR methods can merely capture the pairwise relation information within items and users. To effectively capture the hierarchical information, we propose a novel hierarchical hypergraph neural network to model the hierarchical architecture. Moreover, considering that the items in sessions are sequentially ordered, while the hypergraph can only model the set relation, we propose a directed graph aggregator (DGA) to aggregate the sequential information from the directed global item graph. By attentively combining the embeddings of the above two modules, we propose a framework dubbed H3GNN (Hybrid Hierarchical HyperGraph Neural Network). Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed model compared to the state-of-the-art methods, and ablation experiment results validate the effectiveness of all the proposed components.

Reverse-graph enhanced graph neural networks for session-based recommendation

Session-based recommendation (SBR) aims to predict the next item based on anonymous sessions, which has become a hot research area in recent years. A variety of SBR models have been proposed, and the graph neural network (GNN)-based models are shown to achieve the state-of-the-art performance in SBR. In our research, we observe that there is a decrease in the performance of SBR models for sessions with short length, which hinders the overall performance of SBR models. To alleviate this problem, we propose to add some virtual item(s) into short sessions as the supplementary contextual information to improve model performance. The main challenge lies in how to predict the appropriate virtual item(s) to be added in short sessions. In this paper, we propose Reverse-graph Enhanced Graph Neural Networks (RGE-GNN), a new GNN-based model for SBR. In RGE-GNN, we first construct a reverse graph of a session (RGS) to capture the complex reverse transition relation between items in the session. After that, the reverse prediction is proposed to be conducted to predict the appropriate virtual item(s). As a result, richer information could be carried by the enhanced session, which could lead to higher recommendation accuracy. In addition, to ensure that more relevant virtual item(s) should be added into a session, we propose a preference memory mechanism (PMM) to obtain the expected preference of each session. Extensive experiments conducted on three real-world datasets demonstrate that RGE-GNN outperforms the baselines including the recent SBR models.

Bi-preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation

Session-based recommendation intends to predict next purchased items based on anonymous behavior sequences. Numerous economic studies have revealed that item price is a key factor influencing user purchase decisions. Unfortunately, existing methods for session-based recommendation only aim at capturing user interest preference, while ignoring user price preference. Actually, there are primarily two challenges preventing us from accessing price preference. First, the price preference is highly associated to various item features (i.e., category and brand), which asks us to mine price preference from heterogeneous information. Second, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling. To handle above challenges, we propose a novel approach Bi-Preference Learning Heterogeneous Hypergraph Networks (BiPNet) for session-based recommendation. Specifically, the customized heterogeneous hypergraph networks with a triple-level convolution are devised to capture user price and interest preference from heterogeneous features of items. Besides, we develop a Bi-Preference Learning schema to explore mutual relations between price and interest preference and collectively learn these two preferences under the multi-task learning architecture. Extensive experiments on multiple public datasets confirm the superiority of BiPNet over competitive baselines. Additional research also supports the notion that the price is crucial for the task.

Session-based recommendation by exploiting substitutable and complementary relationships from multi-behavior data

Session-based recommendation by exploiting substitutable and complementary relationships from multi-behavior data

Global heterogeneous graph enhanced category-aware attention network for session-based recommendation

Session-based recommendation (SBR) aims at predicting the next item based on an ongoing recorded session of user’s behaviors. Most of existing approaches mainly focus on capturing various item relations such as transitions, co-occurrences, via item identifiers (IDs). In such model, item attributes, such as category, are simply ignored. A few approaches try to explore items’ category information to design SBR model. However, due to the dynamically changing preferences of users, it is challenging to exactly pinpoint which category of items is currently of interest to the user. We argue that since a single session faces a serious problem of category information sparsity, the category context of other similar sessions can help to model user category interests more accurately. To model user intent more accurately, the intra-category information is vital beside the inter-category user interest modeling.In this paper, we propose a Heterogeneous Graph Enhanced Category-aware Attention Network (HGCAN) as a solution model to accomplish both two tasks of estimating user’s category intent and predicting items for recommendation. The architecture of HGCAN consists of a weighted heterogeneous graph attention network (WHGAT) on global graph for item and category embeddings learning and a category-aware attention network for session representations learning. Experiments on three public datasets validate our HGCAN outperforms these state-of-the-art models.11Source code and data are available at https://github.com/resistzzz/HGCAN.

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.

A Session-Based Recommendation Model That Integrates the Temporal Sequence of Session Interactions and the Global Distance-Awareness of Items with Graph Neural Networks

In the session-based recommendation algorithm, a better class of methods is to model the complex interaction relationship within the session as a graph structure, and then use the graph neural network to capture the deep features of the item from it. However, most models do not deeply mine the effective information contained in the sequence temporal relationship, nor do they pay attention to the auxiliary contribution of the global distance in different sessions to the item representation. This paper proposes a session-based recommendation model that integrates the temporal sequence of session interactions and the global distance-awareness of items with graph neural networks (TSDA-GNN). First, according to all session sequences, a global graph, a session graph, and a feature graph are constructed. In the global graph, the average “spacing” between items is introduced to represent the association between two items in the global graph. In the session graph, the interaction timing of the item is combined as the weight information in the relationship matrix, and the connection matrix of the session graph is constructed, which can more accurately capture the association relationship between items. Finally, by fusing the feature information of the item, using a graph neural network can deeply mine user preferences hidden in the feature information of the item. The model can not only model the information transmission relationship between items in different sessions, but also further deeply mine the hidden information of global domain and item features. Multiple experiments on two benchmark datasets show that the model proposed in this paper achieves the best results in various indicators.

G3SR: Global Graph Guided Session-Based Recommendation.

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendations under the condition that user profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named global graph guided session-based recommendation (G3SR). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items.

Multi-level category-aware graph neural network for session-based recommendation

Graph neural network (GNN)-based models have achieved state-of-the-art performance in session-based recommendation (SBR). In our research, we observe that the subsequence-level intra-category item–item transition can reflect user preference under specific category, thus enhancing recommendation performance. This is overlooked by some existing category-aware SBR models. Moreover, most existing SBR models focus solely on item prediction. However, we find that item prediction and category prediction are closely related, which can respectively serve as the main task and the auxiliary task within a joint multi-task learning (MTL) framework to enhance the performance of the main task. In this paper, we propose an SBR model named Multi-level Category-aware Graph Neural Network (MCGNN) that captures multi-level information involved in a session including subsequence-level intra-category item–item transition, and performs both item prediction and category prediction in an MTL manner. To achieve this, we propose to construct an intra-category graph for each session to model subsequence-level intra-category item–item transition, sequence-level category–category transition and item–category relation. We also construct a sequence graph for each session to model sequence-level item–item transition. These two graphs capture the multi-level information involved in a session. Next, we apply GNNs to learn item representations and category representations through the two graph views. Finally, we perform item prediction and category prediction in a joint MTL framework. Our experimental results on three widely used datasets show the superiority of our proposed MCGNN.

CLHHN: Category-aware Lossless Heterogeneous Hypergraph Neural Network for Session-based Recommendation

In recent years, session-based recommendation (SBR), which seeks to predict the target user’s next click based on anonymous interaction sequences, has drawn increasing interest for its practicality. The key to completing the SBR task is modeling user intent accurately. Due to the popularity of graph neural networks (GNNs), most state-of-the-art (SOTA) SBR approaches attempt to model user intent from the transitions among items in a session with GNNs. Despite their accomplishments, there are still two limitations. First, most existing SBR approaches utilize limited information from short user–item interaction sequences and suffer from the data sparsity problem of session data. Second, most GNN-based SBR approaches describe pairwise relations between items while neglecting complex and high-order data relations. Although some recent studies based on hypergraph neural networks have been proposed to model complex and high-order relations, they usually output unsatisfactory results due to insufficient relation modeling and information loss. To this end, we propose a category-aware lossless heterogeneous hypergraph neural network (CLHHN) in this article to recommend possible items to the target users by leveraging the category of items. More specifically, we convert each category-aware session sequence with repeated user clicks into a lossless heterogeneous hypergraph consisting of item and category nodes as well as three types of hyperedges, each of which can capture specific relations to reflect various user intents. Then, we design an attention-based lossless hypergraph convolutional network to generate sessionwise and multi-granularity intent-aware item representations. Experiments on three real-world datasets indicate that CLHHN can outperform the SOTA models in making a better tradeoff between prediction performance and training efficiency. An ablation study also demonstrates the necessity of CLHHN’s key components.

Self-supervised global graph neural networks with enhance-attention for session-based recommendation

Session-based recommendation is a challenging task which predicts the next click based on the short-term behavior of anonymous users. Compared to other recommendation models, session-based recommendations are more difficult due to the limited amount of available data, which is also data sparsity. To solve the problem, we induce self-supervised learning, which can be incorporated into network training by constructing real samples from raw data. It generates self-supervised signals and maximizes the mutual information of session expressions learned. In addition, we propose an enhanced attention module called Enhance-attention. It combines knowledge from global-level graphs and session-level graphs and enhances the intent representation of sessions using Transformer. In this paper, we propose a new approach, called EAT-SGNN, that is able to predict the next click in a more granular way using all items in the session. The model is augmented by self-supervised learning that generates supervised signals. The model is evaluated on three public datasets: Tmall, Nowplaying, and Diginetica. According to the experimental results, the proposed method achieves state-of-the-art performance. All the code and datasets are publicly available on https://github.com/ch30git798/EAT-SGNN.git.

Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation

Session-based recommendation (SBR) systems aim to utilize the user’s short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-temporal Contrastive Learning-enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph embedding is leveraged to enhance the spatial view in the main task. Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines. We release our source code at https://github.com/SUSTechBruce/RESTC-Source-code .

A large scale benchmark for session-based recommendations on the legal domain

A large scale benchmark for session-based recommendations on the legal domain

Dual cross-domain session-based recommendation with multi-channel integration

Session-based recommendation aims at predicting the next behavior when the current interaction sequence is given. Recent advances evaluate the effectiveness of dual cross-domain information for the session-based recommendation. However, we discover that accurately modeling the session representations is still a challenging problem due to the complexity of preference interactions in the cross-domain, and various methods are proposed to only model the common features of cross-domain, while ignoring the specific features and enhanced features for the dual cross-domain. Without modeling the complete features, the existing methods suffer from poor recommendation accuracy. Therefore, we propose an end-to-end dual cross-domain with multi-channel interaction model (DCMI), which utilizes dual cross-domain session information and multiple preference interaction encoders, for session-based recommendation. In DCMI, we apply a graph neural network to generate the session global preference and local preference. Then, we design a cross-preference interaction module to capture the common, specific, and enhanced features for cross-domain sessions with local preferences and global preferences. Finally, we combine multiple preferences with a bilinear fusion mechanism to characterize and make recommendations. Experimental results on the Amazon dataset demonstrate the superiority of the DCMI model over the state-of-the-art methods.

Causality and Correlation Graph Modeling for Effective and Explainable Session-Based Recommendation

Session-based recommendation, which has witnessed a booming interest recently, focuses on predicting a user’s next interested item(s) based on an anonymous session. Most existing studies adopt complex deep learning techniques (e.g., graph neural networks) for effective session-based recommendation. However, they merely address co-occurrence between items, but fail to distinguish a causality and correlation relationship. Considering the varied interpretations and characteristics of causality and correlation relationships between items, in this study, we propose a novel method denoted as CGSR by jointly modeling causality and correlation relationships between items. In particular, we construct cause, effect, and correlation graphs from sessions by simultaneously considering the false causality problem. We further design a graph neural network–based method for session-based recommendation. To conclude, we strive to explore the relationship between items from specific “causality” (directed) and “correlation” (undirected) perspectives. Extensive experiments on three datasets show that our model outperforms other state-of-the-art methods in terms of recommendation accuracy. Moreover, we further propose an explainable framework on CGSR and demonstrate the explainability of our model via case studies on an Amazon dataset.

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.

Hybrid session-aware recommendation with feature-based models

Session-based recommender systems model the interests of users based on their browsing behavior with the goal of making suitable item suggestions in an ongoing usage session. Most existing work in this growing research area make only use of the most recent observed interactions for each user, and they typically solely rely on user–item interaction data (e.g., click events) for interest modeling. Thus, they do not leverage important forms of other information which are commonly available in practical settings. In this work, we therefore propose a hybrid approach for personalized session-based (“session-aware”) recommendation, which (i) is able to take into account various types of side information as model features and which (ii) can be combined with existing session-based (or session-aware) recommendation models. Technically, our approach is based on stacking several session-based modeling approaches with efficient machine learning methods for tabular data, in our case using Gradient Boosting Machines (GBMs). We successfully evaluated our approach (named HySAR) on two public e-commerce datasets. Specifically, we also demonstrate the effectiveness of a number of novel model features that we engineered in the course of this research. These features, which were mostly unexplored in previous works, relate to various types of information related to the users, their actions, the items, as well as contextual session characteristics. Different existing recommendation approaches and further problem specific features can be easily added in our generic method to improve recommendations.

Noise-reducing graph neural network with intent-target co-action for session-based recommendation

Session-based recommendation (SBR) originates from a real-world need to provide effective recommendation solutions for unlogged users. How to utilize short interaction sequences of anonymous users for practical recommendations has become a critical issue. Current research on SBR generally suffers from noisy terms interfering with prediction, difficulty in capturing long-term dependencies, and ineffective utilization of session-level information. Inspired by the outstanding learning ability of the sparse attention mechanism for distant information and reduced noise through sparse transformations, we propose an SBR model. We combine the graph neural network with sparse graph attention mechanisms to learn enhanced and denoised target information and long-term preferences. In addition, we propose a similar-intent collaboration module that can efficiently utilize session-level collaboration information. Combining the information, the model generates a reliable representation of the session. Our model is superior to the current SOTA model on three widely used datasets (Diginetica, Tmall, RetailRocket). Compared with the best baseline, we improved by 5.09%, 3.86%, and 5.45% with P@20 and by 12.63%, 22.78%, and 3.06% with MRR@20.