Determinantal Point Process Likelihoods for Sequential Recommendation

Recent research has demonstrated the effectiveness of utilizing contrastive learning for training Transformer-based sequence encoders in sequential recommendation tasks. Items are represented using vectors and the relations between items are measured by the dot product self-attention, the feature representation in sequential recommendation can be enhanced. However, in real-world scenarios, user behavior sequences are unpredictable, and the limitations of dot product-based approaches hinder the complete capture of collaborative transferability. Moreover, the Bayesian personalized ranking (BPR) loss function, commonly utilized in recommendation systems, lacks constraints when considering positive and negative sampled items, potentially leading to suboptimal optimization outcomes. This presents a complex challenge that needs to be addressed. To tackle these issues, this article proposes a novel method involving stochastic self-attention. This article introduces uncertainty into the proposed model by utilizing elliptical Gaussian distribution controlled by mean and covariance vector to explain the unpredictability of items. At the same time, the proposed model combines a Wasserstein self-attention module to compute the positional relationships between items within a sequence in order to effectively incorporate uncertainty into the training process. The Wasserstein self-attention mechanism satisfies the triangular inequality and can not only addresses uncertainty but also promote collaborative transfer learning. Furthermore, embedding a stochastic Gaussian distribution into each item will bring additional uncertainty into the proposed model. Multi-pair contrastive learning relies on high-quality positive samples, and the proposed model combines the cloze task mask and dropout mask mechanisms to generate high-quality positive samples. It demonstrates superior performance and adaptability compared to traditional single-pair contrastive learning methods. Additionally, a dynamic loss reweighting strategy is introduced to balance the cloze task loss and the contrastive loss effectively. We conduct experiments and the results show that the proposed model outperforms the state-of-the-art models, especially on cold start items. For each metric, the hit ratio (HR) and normalized discounted cumulative gain (NDCG) on the Beauty dataset improved by an average of 1.3% and 10.27%, respectively; on the Toys dataset improved by an average of 8.24% and 5.89%, respectively; on the ML-1M dataset improved by an average of 68.62% and 8.22%, respectively; and on the ML-100M dataset improved by an average of 93.57% and 44.87% Our code is available at DOI: 10.5281/zenodo.13634624.

Sequential recommender systems, as a specialized branch of recommender systems that can capture users’ dynamic preferences for more accurate and timely recommendations, have made significant progress. Recently, due to increasing concerns about user data privacy, some researchers have implemented federated learning for sequential recommendation, a.k.a., Federated Sequential Recommender Systems (FedSeqRecs), in which a public sequential recommender model is shared and frequently transmitted between a central server and clients to achieve collaborative learning. Although these solutions mitigate user privacy to some extent, they present two significant limitations that affect their practical usability: (1) They require a globally shared sequential recommendation model. However, in real-world scenarios, the recommendation model constitutes a critical intellectual property for platform and service providers. Therefore, service providers may be reluctant to disclose their meticulously developed models. (2) The communication costs are high as they correlate with the number of model parameters. This becomes particularly problematic as the current FedSeqRec will be inapplicable when sequential recommendation marches into a large language model era. To overcome the above challenges, this article proposes a parameter transmission-free federated sequential recommendation framework (PTF-FSR), which ensures both model and data privacy protection to meet the privacy needs of service providers and system users alike. Furthermore, since PTF-FSR only transmits prediction results under privacy protection, which are independent of model sizes, this new federated learning architecture can accommodate more complex and larger sequential recommendation models. Extensive experiments conducted on three widely used recommendation datasets, employing various sequential recommendation models from both ID-based and ID-free paradigms, demonstrate the effectiveness and generalization capability of our proposed framework. To facilitate future research in this direction, we release our code at https://github.com/hi-weiyuan/PTF-FSR .

The sequential recommendation selects and recommends next items for users by modeling their historical interaction sequences, where the chronological order of interactions plays an important role. Most sequential recommendation methods only pay attention to the order information among the interactions and ignore the time intervals information, which leads to the limitations of capturing dynamic user interests. And previous work neglects diversity in order to improve recommendation accuracy. The model Temporal Self-Attention and Multi-Preference Learning (TSAMPL) is proposed to improve sequential recommendation, which learns dynamic and general user interests separately. The proposed temporal gate self-attention network is introduced to learn dynamic user interests, which takes both contextual information and temporal dynamics into account. To model general user interests, we employ a multi-preference matrix to learn users’ multiple types of preferences for improving recommendation diversity. Finally, the interest fusion module combines dynamic user interests (accuracy) and general user interests (diversity) adaptively. The experiments in sequential recommendation confirm our method is superior to all comparison methods, we also study the impact of each component in the model.

Sequential recommendation aims to capture users' dynamic interest and predicts the next item of users' preference. Most sequential recommendation methods use a deep neural network as sequence encoder to generate user and item representations. Existing works mainly center upon designing a stronger sequence encoder. However, few attempts have been made with training an ensemble of networks as sequence encoders, which is more powerful than a single network because an ensemble of parallel networks can yield diverse prediction results and hence better accuracy. In this paper, we present Ensemble Modeling with contrastive Knowledge Distillation for sequential recommendation (EMKD). Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks. To facilitate knowledge transfer between parallel networks, we propose a novel contrastive knowledge distillation approach, which performs knowledge transfer from the representation level via Intra-network Contrastive Learning (ICL) and Cross-network Contrastive Learning (CCL), as well as Knowledge Distillation (KD) from the logits level via minimizing the Kullback-Leibler divergence between the output distributions of the teacher network and the student network. To leverage contextual information, we train the primary masked item prediction task alongside the auxiliary attribute prediction task as a multi-task learning scheme. Extensive experiments on public benchmark datasets show that EMKD achieves a significant improvement compared with the state-of-the-art methods. Besides, we demonstrate that our ensemble method is a generalized approach that can also improve the performance of other sequential recommenders. Our code is available at this link: https://github.com/hw-du/EMKD.

Recent advancements in generative models have positioned them as one of the principal tools for sequential recommendation due to their exceptional sample diversity and generalization capabilities. Among these, diffusion model-based sequential recommenders have achieved remarkable success. However, most existing approaches still face critical challenges, resulting in suboptimal generation quality: (1) They fail to leverage multimodal knowledge for constructing item representations with well-structured distributional characteristics and semantically enriched information; (2) They predominantly rely on discrete diffusion processes, leading to high error accumulation, reduced time efficiency, and constrained controllability in generative sampling. To mitigate these challenges, we propose LSGM4Rec, a novel framework that integrates Large Language Models (LLMs) with advanced multimodal encoding models to establish multimodal fusion embeddings for items. This design ensures distinct distributional characteristics while enabling the incorporation of semantically rich modal features into guidance condition. Furthermore, we pioneer the stochastic differential equations (SDEs) for recommendation, facilitating smooth transitions between data distributions and enabling optimal trade-off between sampling efficiency and generation quality. Extensive experiments on three datasets demonstrate that LSGM4Rec outperforms existing state-of-the-art sequential recommendation methods.

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.

In recommender systems, modeling user-item behaviors is essential for user representation learning. Existing sequential recommenders consider the sequential correlations between historically interacted items for capturing users' historical preferences. However, since users' preferences are by nature time-evolving and diversified, solely modeling the historical preference (without being aware of the time-evolving trends of preferences) can be inferior for recommending complementary or fresh items and thus hurt the effectiveness of recommender systems. In this paper, we bridge the gap between the past preference and potential future preference by proposing the future-aware diverse trends (FAT) framework. By future-aware, for each inspected user, we construct the future sequences from other similar users, which comprise of behaviors that happen after the last behavior of the inspected user, based on a proposed neighbor behavior extractor. By diverse trends, supposing the future preferences can be diversified, we propose the diverse trends extractor and the time-aware mechanism to represent the possible trends of preferences for a given user with multiple vectors. We leverage both the representations of historical preference and possible future trends to obtain the final recommendation. The quantitative and qualitative results from relatively extensive experiments on real-world datasets demonstrate the proposed framework not only outperforms the state-of-the-art sequential recommendation methods across various metrics, but also makes complementary and fresh recommendations.

Recommendation system plays an important role in online web applications. Sequential recommender further models user short-term preference through exploiting information from latest user-item interaction history. Most of the sequential recommendation methods neglect the importance of ever-changing item popularity. We propose the model from the intuition that items with most user interactions may be popular in the past but could go out of fashion in recent days. To this end, this paper proposes a novel sequential recommendation approach dubbed TRec, TRec learns item trend information from implicit user interaction history and incorporates item trend information into next item recommendation tasks. Then a self-attention mechanism is used to learn better node representation. Our model is trained via pairwise rank-based optimization. We conduct extensive experiments with seven baseline methods on four benchmark datasets, The empirical result shows our approach outperforms other state-of-the-art methods while maintains a superiorly low runtime cost. Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.

Recent deep sequential recommendation models often struggle to effectively model key characteristics of user behaviors, particularly in handling sequence length variations and capturing diverse interaction patterns. We propose STAR-Rec, a novel architecture that synergistically combines preference-aware attention and state-space modeling through a sequence-level mixture-of-experts framework. STAR-Rec addresses these challenges by: (1) employing preference-aware attention to capture both inherently similar item relationships and diverse preferences (2) utilizing state-space modeling to efficiently process variable-length sequences with linear complexity, and (3) incorporating a mixture-of-experts component that adaptively routes different behavioral patterns to specialized experts, handling both focused category-specific browsing and diverse category exploration patterns. We theoretically demonstrate how the state space model and attention mechanisms can be naturally unified in recommendation scenarios, where SSM captures temporal dynamics through state compression while attention models both similar and diverse item relationships. Extensive experiments on four real-world datasets demonstrate that STAR-Rec consistently outperforms state-of-the-art sequential recommendation methods, particularly in scenarios involving diverse user behaviors and varying sequence lengths. The implementation code is available anonymously online for easy reproducibility.

Sequential recommendations aim to capture users’ preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user’s historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations.

<h2>Abstract</h2> Recent research in recommender systems has demonstrated the advantages of pairwise ranking in recommendation. In this work, we focus on the state-of-the-art pairwise ranking loss function, Bayesian Personalized Ranking (BPR), and aim to address two of its limitations, namely: (1) the lack of explainability and (2) exposure bias. We propose a recommendation framework that encompasses various loss functions that are based on BPR and which aim to mitigate the aforementioned limitations. Our open-source framework includes code to train and tune state-of-the-art pairwise ranking recommender systems on benchmark datasets and evaluate them based on the three criteria of ranking accuracy, explainability, and popularity debiasing.

Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks. Given a sequence of interacted items, existing DNN-based sequential recommenders commonly embed each item into a unique vector to support subsequent computations of the user interest. However, due to the potentially large number of items, the over-parameterised item embedding matrix of a sequential recommender has become a memory bottleneck for efficient deployment in resource-constrained environments, e.g., smartphones and other edge devices. Furthermore, we observe that the widely-used multi-head self-attention, though being effective in modelling sequential dependencies among items, heavily relies on redundant attention units to fully capture both global and local item-item transition patterns within a sequence. In this paper, we introduce a novel lightweight self-attentive network (LSAN) for sequential recommendation. To aggressively compress the original embedding matrix, LSAN leverages the notion of compositional embeddings, where each item embedding is composed by merging a group of selected base embedding vectors derived from substantially smaller embedding matrices. Meanwhile, to account for the intrinsic dynamics of each item, we further propose a temporal context-aware embedding composition scheme. Besides, we develop an innovative twin-attention network that alleviates the redundancy of the traditional multi-head self-attention while retaining full capacity for capturing long- and short-term (i.e., global and local) item dependencies. Comprehensive experiments demonstrate that LSAN significantly advances the accuracy and memory efficiency of existing sequential recommenders.

DistVAE: Distributed Variational Autoencoder for sequential recommendation

Most sequential recommendation (SR) systems employing graph neural networks (GNNs) only model a user's interaction sequence as a flat graph without hierarchy, overlooking diverse factors in the user's preference. Moreover, the timespan between interacted items is not sufficiently utilized by previous models, restricting SR performance gains. To address these problems, we propose a novel SR system employing a hierarchical graph neural network (HGNN) to model factorial user preferences. Specifically, a timespan-aware sequence graph (TSG) for the target user is first constructed with the timespan among interacted items. Next, all original nodes in TSG are softly clustered into factor nodes, each of which represents a certain factor of the user's preference. At last, all factor nodes' representations are used together to predict SR results. Our extensive experiments upon two datasets justify that our HGNN-based factorial user modeling obtains better SR performance than the state-of-the-art SR models.

Group-aware graph neural networks for sequential recommendation