Session-based Recommender Research Articles

Session context data integration to address the cold start problem in e-commerce recommender systems

Recommender systems play an important role in identifying and filtering relevant products based on the behaviours of users. Nevertheless, recommender systems suffer from the ‘cold-start’ problem, which occurs when no prior information about a new session or a user is available. Many approaches to solving the cold-start problem have been presented in the literature. However, there is still room for improving the performance of recommender systems in the cold-start stage. In this article, we present a novel method to alleviate the cold-start problem in session-based recommender systems. The purpose of this work is to develop a session similarity-based cold-start session alleviation approach for recommendation systems. The developed method uses previous sessions’ contextual and temporal features to find sessions similar to the newly started one. Our results on three different datasets show that, based on the provided Mean Average Precision and Normalised Discounted Cumulative Gain scores, the Session Similarity-based Framework consistently outperforms baseline models in terms of recommendation relevance and ranking quality across three used datasets. Our approach can be used to address the challenges associated with cold start sessions where no previously interacted items are present.

A Personalised Session-Based Recommender System with Sequential Updating Based on Aggregation of Item Embeddings

A Personalised Session-Based Recommender System with Sequential Updating Based on Aggregation of Item Embeddings

Improved session-based recommender systems using curriculum learning

Curriculum Learning (CL) is an effective technique to train machine learning models where the training samples are supplied to the model in an easy-to-hard manner. Similar to human learning, the model can benefit if the data is given in a relevant order. Based on this notion, we propose to apply the concept of CL to the task of session-based recommender systems. Recurrent Neural Networks and transformer-based models have been successfully utilized for this task and shown to be very effective. In these approaches, all training examples are supplied to the model in every iteration and treated equally. However, the difficulty of a training example can vary greatly and the recommendation model can learn better if the data is given according to an easy-to-difficult curriculum. We design various curriculum strategies and show that applying the proposed CL techniques to a given recommendation model helps to improve performance.

Integration of short and long-term interests: A preference aware session-based recommender

Session-based recommenders have gathered tremendous e-commerce and media streaming applications where the task is to predict the next item user would consume based on the session history. In this arena, advancements have been accomplished using deep learning techniques to model users’ long-term and short-term preferences in a session. The long-term module focuses on the entire item set in a session, while the short-term emphasizes a fixed set of items just before making a choice. While users’ short-term preferences in general reflect their immediate actions or intentions, consideration of such preferences, specifically with a fixed item set, makes the prediction task increasingly challenging. In this work, we attempt to capture the short-term behavior of users considering a dynamic window which self-tunes depending on the category of the last few items. A novel gating network that leverages the different drivers such as price, rating, dwell time, etc. and the latent representation of the items and their categories is introduced. The efficacy of our proposed method Gated Session-based Recommender integrating Short and Long-term preferences (GSRSL) has been evaluated against eight recent baselines on four publicly available real-world datasets, Tmall, Diginetica, and Cosmetics. Our findings demonstrate a significant improvement in MRR, Hit, and NDCG of up to 13.62%, 11.46%, and 13.00%, respectively, across all the datasets.

Session-based recommendations with sequential context using attention-driven LSTM

A Session-based recommender system (SBRS) captures the dynamic behavior of a user to provide recommendations for the next item in the current session. On providing the user’s past interactions of ongoing sessions, the SBRS predicts the next item that a user is likely to interact with. Sessions can vary in duration, from minutes to hours. Many recommender systems prioritize longer sessions, but most datasets have more short sessions. Predicting the next item in short sessions is challenging due to limited context. Additionally, obtaining item embeddings is problematic due to the data sparsity issue in most SBRS, as they rely on one-hot encoding. A long short-term memory (LSTM) with an attention mechanism has been proposed to overcome the abovementioned issues by utilizing LSTM to capture sequential context and incorporating an attention mechanism to focus on the target items. Additionally, to overcome the data sparsity problem, the Word2Vec embedding technique has been used. The proposed model was tested on two publicly available datasets i.e., 30Music and RSC19, and results are compared with basic sequence models i.e., RNN and LSTM. LSTM achieved a 41.95% hit rate on the 30Music, while LSTM-Attention achieved 81.47% on RSC19. In summary, LSTM outperformed RNN and LSTM-Attention on 30Music, whereas LSTM with attention outperformed the other models on RSC19.

Heterogeneous Information Crossing on Graphs for Session-Based Recommender Systems

Recommender systems are fundamental information filtering techniques to recommend content or items that meet users’ personalities and potential needs. As a crucial solution to address the difficulty of user identification and unavailability of historical information, session-based recommender systems provide recommendation services that only rely on users’ behaviors in the current session. However, most existing studies are not well-designed for modeling heterogeneous user behaviors and capturing the relationships between them in practical scenarios. To fill this gap, in this article, we propose a novel graph-based method, namely H eterogeneous I nformation C rossing on G raphs (HICG). HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users’ current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs. In addition, we also propose an enhanced version, named HICG-CL, which incorporates the contrastive learning (CL) technique to enhance item representation ability. By utilizing the item co-occurrence relationships across different sessions, HICG-CL improves the recommendation performance of HICG. We conduct extensive experiments on three real-world recommendation datasets, and the results verify that (i) HICG achieves state-of-the-art performance by utilizing multiple types of behaviors on the heterogeneous graph. (ii) HICG-CL further significantly improves the recommendation performance of HICG by the proposed contrastive learning module.

Using personalized next session to improve session-based recommender systems

Using personalized next session to improve session-based recommender systems

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.

Understanding Diversity in Session-based Recommendation

Current session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve diversity beyond accuracy. Meanwhile, it is unclear how the accuracy-oriented SBRSs perform in terms of diversity. In addition, the asserted “tradeoff” relationship between accuracy and diversity has been increasingly questioned in the literature. Toward the aforementioned issues, we conduct a holistic study to particularly examine the recommendation performance of representative SBRSs w.r.t. both accuracy and diversity, striving for better understanding of the diversity-related issues for SBRSs and providing guidance on designing diversified SBRSs. Particularly, for a fair and thorough comparison, we deliberately select state-of-the-art non-neural, deep neural, and diversified SBRSs by covering more scenarios with appropriate experimental setups, e.g., representative datasets, evaluation metrics, and hyper-parameter optimization technique. The source code can be obtained via github.com/qyin863/Understanding-Diversity-in-SBRSs . Our empirical results unveil that (1) non-diversified methods can also obtain satisfying performance on diversity, which can even surpass diversified ones, and (2) the relationship between accuracy and diversity is quite complex. Besides the “tradeoff” relationship, they can be positively correlated with each other, that is, having a same-trend (win–win or lose–lose) relationship, which varies across different methods and datasets. Additionally, we further identify three possible influential factors on diversity in SBRSs (i.e., granularity of item categorization, session diversity of datasets, and length of recommendation lists) and offer an intuitive guideline and a potential solution regarding learned item embeddings for more effective session-based recommendation.

FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems

When only users’ preferences and interests are considered by a recommendation algorithm, it will lead to the severe long-tail problem over items. Therefore, the unfair exposure phenomenon of recommended items caused by this problem has attracted widespread attention in recent years. For the first time, we reveal the fact that there is a more serious unfair exposure problem in session-based recommender systems (SRSs), which learn the short-term and dynamic preferences of users from anonymous sessions. Considering the fact that in SRSs, recommendations are provided multiple times and item exposures are accumulated over interactions in a session, we define new metrics both for the fairness of item exposure and recommendation quality among sessions. Moreover, we design a dynamic F airness- A ssurance ST rategy for s E ssion-based R ecommender systems ( FASTER ). FASTER is a post-processing strategy that tries to keep a balance between item exposure fairness and recommendation quality. It can also maintain the fairness of recommendation quality among sessions. The effectiveness of FASTER is verified on three real-world datasets and five original algorithms. The experiment results show that FASTER can generally reduce the unfair exposure of different session-based recommendation algorithms while still ensuring a high level of recommendation quality.

Modeling Tradeoffs Using Preference-Based Feedback in Session-Based Recommender Systems

Cold-start is a challenge in most data-driven approaches across various paradigms of recommender systems research. Knowledge integration is becoming essential to alleviate the problem. Despite the availability of knowledge representations like the knowledge graph, there is a need for a richer semantic model to capture relationships among products in a domain. Recommender systems inspired by case-based reasoning uses rich domain knowledge in their recommendation process. Conversational case-based reasoning recommender systems (CCBR-RS) are session-based recommender systems that operate under cold-start assumptions. User preferences in such scenarios can only be learned based on the context of the current session. Tradeoffs have been used in literature to model the relationship among products in recommending appropriate products. The models in earlier works have been based on crisp sets, which lack flexibility in capturing varying degrees of tradeoffs which is a more natural way of dealing with tradeoffs. This work proposes a flexible tradeoff representation scheme based on fuzzy sets to model tradeoffs in terms of linguistic terms and a fuzzy inference system that measures the similarity among tradeoffs represented in linguistic terms. We demonstrate the performance improvement in utilizing a more flexible model of tradeoffs by evaluating the methods on three datasets in a CCBR-RS framework.

A Flexible Session-Based Recommender System for e-Commerce

Research into session-based recommendation systems (SBSR) has attracted a lot of attention, but each study focuses on a specific class of methods. This work examines and evaluates a large range of methods, from simpler statistical co-occurrence methods to embeddings and SotA deep learning methods. This paper analyzes theoretical and practical issues in developing and evaluating methods for SBSR in e-commerce applications, where user profiles and purchase data do not exist. The major tasks of SBRS are reviewed and studied, namely: prediction of next-item, next-basket and purchase intent. For physical retail shopping where no information about the current session exists, we treat the previous baskets purchased by the user as previous sessions drawn from a loyalty system. Mobile application scenarios such as push notifications and calling tune recommendations are also presented. Recommender models using graphs, embeddings and deep learning methods are studied and evaluated in all SBRS tasks using different datasets. Our work contributes a number of very interesting findings. Among all tested models, LSTMs consistently outperform other methods of SBRS in all tasks. They can be applied directly because they do not need significant fine-tuning. Additionally, they naturally model the dynamic browsing that happens in e-commerce web applications. On the other hand, another important finding of our work is that graph-based methods can be a good compromise between effectiveness and efficiency. Another important conclusion is that a “temporal locality principle” holds, implying that more recent behavior is better suited for prediction. In order to evaluate these systems further in realistic environments, several session-based recommender methods were integrated into an e-shop and an A/B testing method was applied. The results of this A/B testing are in line with the experimental results, which represents another important contribution of this paper. Finally, important parameters such as efficiency, application of business rules, re-ranking issues, and the utilization of hybrid methods are also considered and tested, providing comprehensive useful insights into SBRS and facilitating the transferability of this research work to other domains and recommendation scenarios.

Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations

The sequential recommendation (also known as the next-item recommendation), which aims to predict the following item to recommend in a session according to users’ historical behavior, plays a critical role in improving session-based recommender systems. Most of the existing deep learning-based approaches utilize the recurrent neural network architecture or self-attention to model the sequential patterns and temporal influence among a user's historical behavior and learn the user's preference at a specific time. However, these methods have two main drawbacks. First, they focus on modeling users’ dynamic states from a user-centric perspective and always neglect the dynamics of items over time. Second, most of them deal with only the first-order user-item interactions and do not consider the high-order connectivity between users and items, which has recently been proved helpful for the sequential recommendation. To address the above problems, in this article, we attempt to model user-item interactions by a bipartite graph structure and propose a new recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) for the sequential recommendation. PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator. Also, it realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions. To demonstrate the effectiveness of PTGCN, we carried out a comprehensive evaluation of PTGCN on three real-world datasets of different sizes compared with a few competitive baselines. Experimental results indicate that PTGCN outperforms several state-of-the-art sequential recommendation models in terms of two commonly-used evaluation metrics for ranking. In particular, it can make a better trade-off between recommendation performance and model training efficiency, which holds great potential for online session-based recommendation scenarios in the future.

Purpose tendency-aware diversified strategy for effective session-based recommendation

Session-Based Recommender Systems (SBRSs) process time-aware user–item interactions to capture users’ dynamic preferences. Most of the existing SBRSs mainly strive to improve recommendation accuracy by exploiting different deep learning techniques to represent each user on the basis of the most recent session. However, they generally ignore to capture the diversity preference at the session level. In this view, we consider both recommendation diversity and accuracy when generating recommendations. Particularly, we treat that users’ preferences towards diversity might be varied across users (sessions). Thus, we propose an end-to-end neural network model, namely Purpose Tendency-aware Diversified Strategy for Session-based Recommendation (PTDS-SR), where we design a Purpose Tendency Probability (PTP) module matching with a two-channel decoder to guide whether to recommend similar or diversified items given a session. We thus obtain a relatively personalized strategy for each session to exploit the diversity to facilitate recommendation accuracy regarding short-term user preferences (within a session). We compare our approach with representative, state-of-the-art baselines on three real-world datasets, in terms of recommendation accuracy, diversity and a comprehensive metric (considering both accuracy and diversity). Experimental results demonstrate the effectiveness of our approach over the state-of-the-art approaches. Meanwhile, with PTDS-SR, more purposeless sessions (e.g., searching products of various categories) will obtain more diverse recommendation list, and vice versa.

Context-Aware and Click Session-Based Graph Pattern Mining With Recommendations for Smart EMS Through AI

In the field of Artificial Intelligence (AI), Smart Enterprise Management Systems (Smart EMS) and big data analytics are the most prominent computing technologies. A key component of the Smart EMS system is E-commerce, especially Session-based Recommender systems (SRS), which are typically utilized to enhance the user experience by providing recommendations analyzing user behavior encoded in browser sessions. Also the work of the recommender is to predict users’ next actions (click on an item) using the sequence of actions in the current session. Current developments in session-based recommendation have primarily focused on mining more information accessible within the current session. On the other hand, those approaches ignored sessions with identical context for the current session that includes a wealth of collaborative data. Therefore this paper proposed Context-aware and Click session-based graph pattern mining with recommendations for Smart EMS through AI. It employs a novel Triple Attentive Neural Network (TANN) for SRS. Specifically, TANN contains three main components, i.e., Enhanced Sqrt-Cosine Similarity based Neighborhood Sessions Discovery (NSD), Frequent Subgraph Mining (FSM) using Neighborhood Click session-based graph pattern mining and Top-K possible Next-clicked Items Discovery (TNID). The NSD module uses a session-level attention mechanism to find m most similar sessions of the query session, and the FSM module also extracts the frequent subgraphs from the already discovered m most similar sessions of the query session via item-level attention. Then, TNID module is used to discover the top-K possible next-clicked items using the NSD and FSM module via a target-level attention. Finally, we perform comprehensive experiments on one big dataset, DIGINETICA, to verify the effectiveness of the TANN model, and the results of this experiment clearly illustrate the performance of TANN.

A RNN-Based Model to Increase the Diversity in a Session-based Recommender System

A RNN-Based Model to Increase the Diversity in a Session-based Recommender System

FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems

FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems

FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems

FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems

Fusing frequent sub-sequences in the session-based recommender system

A session-based recommender system (RS) forecasts the item to be clicked next by taking advantage of the item sequence in the current session. The deep learning models, such as the recurrent neural network (RNN) and the graph neural network (GNN), are recently applied in the session-based recommendation. However, to the best of our knowledge, the existing methods ignore the fusion of the frequent sub-sequences which refer to those item sub-sequences that appear frequently in different sessions. Intuitively, the more frequently an item sequence appears, the more important it could be. In this paper, we propose the frequent sub-sequence modeling (FSM) method, by utilizing both the local and global information of item sequences. More specifically, we use the item sequence in the current session to build a local session graph and the frequent sub-sequences to construct a global session graph. In addition, we design a gated layer to control the contributions of these two session graphs. Our method is capable of learning both the session-level and global-level item embedding, and thus supports accurate predictions. It is tested on four benchmark datasets and the results show that our method is superior to state-of-the-art approaches consistently.

DCFGAN: An adversarial deep reinforcement learning framework with improved negative sampling for session-based recommender systems

In recent years, with the development of Internet technology, recommender systems have been widely used by virtue of their ability to meet the personalized needs of users. In order to make full use of users’ interactive behaviors, session-based recommender systems have attracted growing research interest. In previous session-based recommender systems, users' historical interactive behavior is utilized to train and update users' preferences, but users' responses to the current recommendation results (immediate feedback) are not effectively exploited to optimize the recommendation strategy. This leads to the decrease of subsequent recommendation accuracy. Aiming at this problem, based on the Recurrent Neural Network (RNN), this paper combines Reinforcement Learning (RL) and Generative Adversarial Networks (GANs). We fully exploit the users’ immediate feedback with RL, and simultaneously take advantage of GAN to satisfy the requirement of training data brought by RL. Furthermore, we optimize the negative sampling method and propose Deep Generative Adversarial Networks-based Collaborative Filtering (DCFGAN). The experimental results show that this algorithm can effectively improve the recommendation accuracy in session-based recommender systems.