Long-term Preferences Research Articles

PRMNBR: Personalized Recommendation Model for Next Basket Recommendation Using User’s Long-Term Preference, Short-Term Preference, and Repetition Behaviour

PRMNBR: Personalized Recommendation Model for Next Basket Recommendation Using User’s Long-Term Preference, Short-Term Preference, and Repetition Behaviour

Skip-Gram and Transformer Model for Session-Based Recommendation

Session-based recommendation uses past clicks and interaction sequences from anonymous users to predict the next item most likely to be clicked. Predicting the user’s subsequent behavior in online transactions becomes a problem mainly due to the lack of user information and limited behavioral information. Existing methods, such as recurrent neural network (RNN)-based models that model user’s past behavior sequences and graph neural network (GNN)-based models that capture potential relationships between items, miss different time intervals in the past behavior sequence and can only capture certain types of user interest patterns due to the characteristics of neural networks. Graphic models created to improve the current session reduce the model’s success due to the addition of irrelevant items. Moreover, attention mechanisms in recent approaches have been insufficient due to weak representations of users and products. In this study, we propose a model based on the combination of skip-gram and transformer (SkipGT) to solve the above-mentioned drawbacks in session-based recommendation systems. In the proposed method, skip-gram both captures chained user interest in the session thread through item-specific subreddits and learns complex interaction information between items. The proposed method captures short-term and long-term preference representations to predict the next click with the help of a transformer. The transformer in our proposed model overcomes many limitations in turn-based models and models longer contextual connections between items more effectively. In our proposed model, by giving the transformer trained item embeddings from the skip-gram model as input, the transformer has better performance because it does not learn item representations from scratch. By conducting extensive experiments with three real-world datasets, we confirm that SkipGT significantly outperforms state-of-the-art solutions with an average MRR score of 5.58%.

Novel behavior-enhanced long- and short-term interest model for sequential recommendation

In the realm of modern recommender systems, user-item interaction data often exhibit sequential patterns in relation to various behaviors, such as clicks and purchases on e-commerce platforms. The objective of heterogeneous sequential recommendation (HSR) is to predict each user's next item of interest under a specific behavior based on these interactions. However, existing approaches to HSR struggle to fully capture item transition relationships, as they consider these relationships from a single dimension and at a coarse-grained level. To bridge this gap, we propose the novel Behavior-enhanced Long- and Short-term Interest (BLSI) model, which explores fine-grained item transition relationships in both local and global dimensionalities. At its core, BLSI incorporates a behavior-enhanced self-attention network (BSAN) to capture short-term user preferences. BSAN distinguishes the effects of different behaviors and considers cross-type behavior influences during the linear projection and attention score calculation stages. Additionally, BLSI employs a heterogeneous graph neural network (HGNN) to model long-term user interests by discriminatively aggregating the information of neighboring nodes according to their behavior transition relationships. Furthermore, a gating mechanism is implemented to adaptively fuse short- and long-term preferences for personalized recommendations. Extensive experimental results on three datasets demonstrate that BLSI significantly outperforms state-of-the-art recommendation methods, highlighting the advantages of leveraging sequentiality and behavioral heterogeneity.

Dual-Tower Counterfactual Session-Aware Recommender System.

In the complex dynamics of modern information systems such as e-commerce and streaming services, managing uncertainty and leveraging information theory are crucial in enhancing session-aware recommender systems (SARSs). This paper presents an innovative approach to SARSs that combines static long-term and dynamic short-term preferences within a counterfactual causal framework. Our method addresses the shortcomings of current prediction models that tend to capture spurious correlations, leading to biased recommendations. By incorporating a counterfactual viewpoint, we aim to elucidate the causal influences of static long-term preferences on next-item selections and enhance the overall robustness of predictive models. We introduce a dual-tower architecture with a novel data augmentation process and a self-supervised training strategy, tailored to tackle inherent biases and unreliable correlations. Extensive experiments demonstrate the effectiveness of our approach, outperforming existing benchmarks and paving the way for more accurate and reliable session-based recommendations.

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.

Diarec: Dynamic Intention-Aware Recommendation with Attention-Based Context-Aware Item Attributes Modeling

Abstract Recommender systems (RSs) often focus on learning users’ long-term preferences, while the sequential pattern of behavior is ignored. On the other hand, sequential RSs try to predict the next action by exploring relations between items in a user’s last interactions but do not consider the general preference. Recently, the performance of RSs has increased by unifying these two types of paradigms. However, existing methods still have two limitations. First, the user’s behavior uncertainty impedes precise learning of preferences. Second, being unable to understand the semantics of items makes the effect of the same item considered in the same way. These limitations jointly prevent RS from learning multifaceted preferences to capture the actual intentions of users. Existing methods have not properly addressed these problems since they ignore context-aware interactions between the user and item in terms of the links between the user and item attributes and sequential user actions over time. To address these challenges, this paper proposes a novel model, called the Dynamic Intention-Aware Recommendation with attention-based context-aware item attributes modeling (DIARec), which is capable of determining users’ preferences based on their goal intention, taking into account the influence of various item features on user decision-making in their current context. Specifically, to model users’ dynamic intentions, we introduce a dynamic intent-aware module to represent the hierarchical relations between items and their attributes in a given session. Experiments on benchmark datasets indicate that the proposed model DIARec outperforms other state-of-the-art methods.

Integrating user short-term intentions and long-term preferences in heterogeneous hypergraph networks for sequential recommendation

Sequential recommendation tries to model the binary correlations among users and items in a sequence to provide accurate recommendations. However, user behaviors are influenced by both their intentions and preferences. Existing sequential recommendation models cannot effectively capture the user’s real preferences and intentions just from the interaction data. To tackle this dilemma, we propose IPSRec, an innovative sequential recommendation approach that explicitly mines user intentions and preferences by fusing heterogeneous auxiliary information from multiple sources. IPSRec employs a heterogeneous hypergraph network to fuse and propagate the topological information of users’ short-term intentions and long-term preferences. Subsequently, IPSRec engages a locally reinforced attention structure to learn the migration process of users’ short-term intentions and long-term preferences in sequences. Lastly, we combine the user intentions and preferences for the final recommendation. Experiments conducted on various datasets present the preeminence of our approach over several state-of-the-art methods. Moreover, the results corroborate the validity of the feature modeling for user intentions and preferences in accurately representing users’ purchase behaviors.

Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration

This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary advancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address the existing models’ rigidity by dynamically blending short-term (most recent) and long-term (historical) preferences, moving beyond static period definitions. Our approaches, pre-combination (LCII-Pre) and post-combination (LCII-Post), with fixed (Fix) and flexible learning (LP) weight configurations, are thoroughly evaluated. We conducted extensive experiments to assess our models’ performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-PreFix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4Rec+st+TSA, respectively. On the Steam dataset, LCII-PostLP outperformed these models by 18.66% and 5.5%. Furthermore, on the Amazon dataset, LCII showed a 2.59% and 1.89% improvement in Recall@20 over II-RNN and CAII. These results affirm the significant enhancement our models bring to session-aware recommendation systems, showcasing their potential for both academic and practical applications in the field.

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.

Applications and Ethical Challenges of Artificial Intelligence Technologies in E-Commerce

Abstract The theory and technology of artificial intelligence are becoming more and more mature, and its application fields are expanding, greatly changing people’s lifestyles and work modes. The article sorted out the power mechanism and interaction relationship of AI technology to promote the development of e-commerce, and innovated the development mode of e-commerce based on AI empowerment, but also illustrated the ethical challenges brought by its application. Combining the BERT model and LSTM model to establish a sentiment analysis model for online reviews of e-commerce products, using the BiGRU model to obtain users’ long-term preferences, and learning users’ short-term preferences through the multi-head self-attention mechanism to establish a personalized recommendation model for e-commerce products. The study explores the algorithmic discrimination of AI-enabled e-commerce and introduces economic concepts to analyze the consumer’s perceived betrayal and kill-off phenomenon through experiments. At the lowest level, the individualized recommendation model only has an MAE value of 0.679, while the BERT-LSTM model has a sentiment analysis accuracy of 0.837. The average level of perceived betrayal and deliberate attribution of the low-priced group is 4.035 and 4.369, respectively, which is lower than that of the high-price group is lower, and the killing phenomenon brought by algorithmic price discrimination can be alleviated when the elasticity of demand is 0.4.AI-enabled e-commerce needs to be legally regulated from the five perspectives of legislation, law enforcement, judiciary, law-abiding, and law-monitoring as a way of circumventing the algorithmic discrimination and ethical challenges brought about by AI technology.

An Intention-Aware Markov Chain Based Method for Top-K Recommendation

Recommender systems play significant roles in business, especially in e-commerce. Nevertheless, users’ behaviors are usually mixing and drifting, which is hard to tackle. Current sequential methods of item-wise interest extracting suffer from the intricacy and sparsity of data. Inspired by that category-wise interests may be intrinsic drivers of user behaviors and heterogeneous actions may reveal certain behavior patterns, we introduce intention as a tuple of category and action to address the data issues. In this paper, an intention-aware Markov chain based sequential recommendation model (IMRec) is proposed. We model the overall preferences of users as the integration of long-term preferences and short-term intents. In particular, the matrix factorization method is adopted to extract the long-term user-item preference. For the modeling of the short-term intents transition, we adopt high-order Markov chain based methods. A factorized mixture transition distribution model for high-order Markov chain approximation is leveraged in this paper to reduce the algorithm complexity. An auxiliary loss on intention representation is utilized, which brings considerable performance improvements. Experiments on real-world datasets demonstrate that our model outperforms state-of-the-art baseline models in terms of three common metrics, and shows superior stability, scalability, and training efficiency. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In e-commerce, sequential recommenders are essential to facilitate decision-making and promote business. A big challenge is to capture the sequential patterns from the mixing and drifting user-item interaction sequences. Customer behaviors are often driven by their inherent intentions, which may present more stable and reliable patterns. Motivated by that, we propose a novel intention-aware next-item recommendation algorithm with high performance. Our method models the user-item preferences as the integration of long-term preferences and short-term intents. More specifically, long-term preferences show the general tastes of users, which are modeled by latent factors of users and items. Additionally, the intention is defined as a tuple of category and action. We model the short-term intents as the intention transition from the past intentions by a high-order Markov chain. We further leverage a factorization-based mixture transition distribution model for high-order Markov chain approximation and reduce the algorithm complexity. The proposed model is validated in 4 real-world datasets and shows good recommendation performance, performance stability, model scalability, and training efficiency. Our model provides an effective recommendation method at low computational costs for e-commerce companies.

FSASA: Sequential recommendation based on fusing session-aware models and self-attention networks

The recommendation system can alleviate the problem of ?information overload?, tap the potential value of data, push personalized information to users in need, and improve information utilization. Sequence recommendation has become a hot research direction because of its practicality and high precision. Deep Neural Networks (DNN) have the natural advantage of capturing comprehensive relations among different entities, thus almost occupying a dominant position in sequence recommendation in the past few years. However, as Deep Learning (DL)-based methods are widely used to model local preferences under user behavior sequences, the global preference modeling of users is often underestimated, and usually, only some simple and crude user latent representations are introduced. Therefore, this paper proposes a sequential recommendation based on Fusing Session-Aware models and Self-Attention networks (FSASA). Specifically, we use the Self-Attentive Sequential Recommendation (SASRec) model as a global representation learning module to capture long-term preferences under user behavior sequences and further propose an improved session-aware sequential recommendation model as a local learning representation module from user model the user?s dynamic preferences in the historical behavior, and finally use the Gated Recurrent Unit (GRU) module to calculate their weights. Experiments on three widely used recommendation datasets show that FSASA outperforms state-of-the-art baselines on two commonly used metrics.

Long-Term Preference Mining With Temporal and Spatial Fusion for Point-of-Interest Recommendation

Long-Term Preference Mining With Temporal and Spatial Fusion for Point-of-Interest Recommendation

Protests, long-term preferences, and populism: Evidence from 1968 in Europe

Abstract In 1968, young people grew up in an atmosphere of strong dissatisfaction and distrust against the status quo. We show that higher exposure to protests in 1968 leads to higher dissatisfaction toward national governments and raises the probability of voting for populist parties. Consistently with the impressionable years hypothesis, we find these effects valid only for those aged between 18 and 25 years during 1968. Our results are robust to a series of placebo tests and to alternative definitions of our treatment and control groups. We find that our results are driven by individuals with a middle or low level of education. We also find suggestive evidence that the mechanisms driving our results can depend on individuals’ level of education.

Housing advantage, hidden curriculum, habitus: students’ past and future housing pathways revisited

ABSTRACT While past research into young people’s transitions out of the parental home identified a distinct student housing pathway offering an institutionally supported ‘housing advantage’, more recently scholars have pointed to widespread housing precarity among university students, reconceptualising the housing challenges students face as a ‘hidden curriculum’ that reinforces inequalities. Meanwhile, time spent navigating this hidden curriculum in increasingly widespread purpose-built student accommodations (PBSA) has the potential to reshape the student habitus, fostering future preferences for the high-density, privatized urban space PBSA represents. This paper re-examines these notions, drawing on interviews with 27 students in Waterloo, Canada, regarding their past experiences and future expectations of housing. While the interviews reveal a multitude of pathways, concepts of housing advantage and hidden curriculum are not as contradictory as they may appear, with many students benefitting from supports offered by university residences before facing an expensive, discriminatory and predatory rental market. Although students’ experiences normalized high-density living, they did not necessarily supersede long-term preferences for detached home ownership, and access to amenities was more important than private space as such.

CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation

Providing interpretable explanations can notably enhance users’ confidence and satisfaction with regard to recommender systems. Counterfactual explanations demonstrate remarkable performance in the realm of explainable sequential recommendation. However, current counterfactual explanation models designed for sequential recommendation overlook the temporal dependencies in a user’s past behavior sequence. Furthermore, counterfactual histories should be as similar to the real history as possible to avoid conflicting with the user’s genuine behavioral preferences. This paper presents counterfactual explanations by Considering temporal dependencies (CETD), a counterfactual explanation model that utilizes a variational autoencoder (VAE) for sequential recommendation and takes into account temporal dependencies. To improve explainability, CETD employs a recurrent neural network (RNN) when generating counterfactual histories, thereby capturing both the user’s long-term preferences and short-term behavior in their real behavioral history. Meanwhile, CETD fits the distribution of reconstructed data (i.e., the counterfactual sequences generated by VAE perturbation) in a latent space, and leverages learned variance to decrease the proximity of counterfactual histories by minimizing the distance between the counterfactual sequences and the original sequence. Thorough experiments conducted on two real-world datasets demonstrate that the proposed CETD consistently surpasses current state-of-the-art methods.

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.

DeepCPR: Deep Path Reasoning Using Sequence of User-Preferred Attributes for Conversational Recommendation

Conversational recommender systems (CRS) have garnered significant attention in academia and industry because of their ability to capture user preferences via system questions and user responses. Typically, in a CRS, reinforcement learning (RL) is utilized to determine the optimal timing for requesting attribute information or suggesting items. However, existing methods consider user-preferred attributes independently and ignore that attributes may be of different importance to the same user, in the attribute and item selection phases, which limits the accuracy and interpretability of CRS. Inspired by this, we propose deep conversational path reasoning (DeepCPR), which involves constructing a reasoning path on a graph with a series of user-favored attributes. It utilizes the attention mechanism to thoroughly examine the connections between these attributes and provide improved explanations for which attributes to inquire about or which items to recommend. In DeepCPR, two deep-learning-based modules are proposed to realize attribute and item selection. In the first module, the sequence of attributes confirmed by the user in conversation is encoded with a gated graph neural network to obtain the user’s long-term preference using a self-attention mechanism for the selection of candidate attributes. In the second module, a self-attention approach with more appropriate strategies is developed to dynamically select candidate items. In addition, to achieve fine-grained user preference modeling, a recurrent neural network is employed to aggregate the sequence of attributes that interact with the users. Numerous experimental evaluations conducted on four real CRS datasets show that the proposed method significantly outperforms existing advanced methods in terms of conversational recommendations.

Multi-temporal Sequential Recommendation Model Based on the Fused Learning Preferences

Since sequential recommendation can effectively capture the user’s dynamic interaction pattern according to the user’s historical interaction, it plays one of the important roles in the field of recommendation systems. Nowadays, the sequential recommendation models based on deep learning approaches have achieved good results in reality due to their excellent performance in information retrieval and filtering. However, there are still some challenges that still should be overcome of them. A major disadvantage of them is that they are only considering users’ short-time interests while ignoring their long-term preferences. Moreover, they are incapable of considering the influence of time information in the original interaction sequence, which could be helpful to fully extract various patterns via the different temporal embedding forms. Therefore, this paper proposes a novel model named multi-temporal sequential recommendation model based on the fused learning preference named MTSR-FLP in short. In the proposed framework, this paper adopts an MLP-based state generation module to consider the user’s long-term preferences and the short-time interests simultaneously. In particular, the proposed MTSR-FLP model designs a global representation learning approach to obtain the user’s global preference, and a local representation learning approach to capture the users’ local preference via its historical information. Moreover, the proposed model develops a multiple temporal embedding scheme to encode the positions of user–item interactions of a sequence, in which multiple kernels are utilized for the absolute or relative timestamps to establish unique embedding matrices. Finally, compared with other advanced sequence recommendation models on five public real-world datasets, the experimental results show that the proposed MTSR-FLP model has improved the performance of HR@10 from the 6.68% through 31.10% and NDCG@10 from the 8.60% through 42.54%.

Dynamic Multi-Objective Optimization Framework With Interactive Evolution for Sequential Recommendation

In contrast to traditional recommender systems which usually pay attention to users' general and long-term preferences, sequential recommendation (SR) can model users' dynamic intents based on their behaviour sequences and suggest the next item(s) to them. However, most of existing sequential models learn the ranking score of an item only based on its relevance property, and the personalized user demands in terms of different learning objectives, such as diversity, tail novelty or recency, which have been played essential roles in multi-objective recommendation (MOR), are often neglected in SR. In this paper, we first discuss the importance of considering multiple different objectives within a learning model for recommender system. Next, to capture users' objective-level preferences by utilizing interactive information in the sequential context, we propose a novel Dynamic Multi-objective Recommendation (DMORec) framework with interactive evolution for SR. In particular, DMORec formulates a dynamic multi-objective optimization task to simultaneously optimize more than two varying objectives in an interactive recommendation process. Moreover, to resolve this optimization task in SR, we develop an evolutionary algorithm with supervised learning approach to obtain the Pareto-optimal solutions of the formulated problem. Comprehensive experiments on four real-world datasets demonstrate the effectiveness of the proposed DMORec for dynamic multi-objective recommendation in sequential recommender systems.