The Point-of-Interest (POI) recommender systems utilize users’ historical check-in sequences to predict future visits and are widely used in location-based social service platforms. However, users’ check-in behaviors often exhibit strong spatial constraints and temporal dependencies, as users typically visit only a limited set of POIs. This leads to a long-tail distribution and severe data sparsity, which obscures the contribution of individual decision factors such as specific POI characteristics, temporal preferences, and geographic proximity, and limits model generalization due to insufficient fine-grained supervision. To address these issues, we propose a multi-factor generative adversarial framework, GANPOI, that integrates a multi-head discriminator within a generative adversarial network architecture to mitigate data sparsity and improve recommendation accuracy. Specifically, the proposed model consists of two primary components: (1) the generator module models user trajectories using a Transformer-based architecture and employs adversarial training to capture self-supervised signals from check-in contexts; and (2) a multi-head discriminator module composed of several sub-discriminators, each performing task-specific discrimination from different semantic perspectives. This structure enhances the model’s ability to capture fine-grained contextual factors influencing user decisions. Extensive experiments on three real-world datasets demonstrate that GANPOI consistently outperforms existing state-of-the-art POI recommendation methods. To facilitate reproducibility, we have released both the code and the POI recommendation datasets we used. The source codes and datasets are available at https://anonymous.4open.science/r/GANPOI-YYR.

The objective of the Point-of-Interest (POI) recommendation system is to predict potential future visits based on users' check-in histories. However, due to factors such as users' tendency to visit nearby locations, time and space mobility costs, users are more likely to visit a limited number of POIs within a confined geographical area. Consequently, POI recommendation faces a more severe data sparsity issue compared to other recommendation scenarios. Current research has shown that incorporating multimodal content information into POI recommendations can effectively alleviate the data sparsity problem. However, existing methods still have the following limitations: 1) Multimodal noise hinders the effective extraction of multimodal content. 2) Multimodal feature fusion ignores the differential impact of various modalities on user decisions. To address these issues, we propose an Interest-aware MultiModal adaptive fusion framework for POI recommendations (IMMPOI). Specifically, we propose an interest-oriented purifier to perform multimodal noise filtering based on user preference, and introduce a disentangled multimodal graph encoder to accurately capture fine-grained behavior features, multimodal features, geographical, and sequential relationships between users and POIs. Then, we develop an interest-aware multimodal fuser that learns comprehensive multimodal representations of users and POIs by adaptively integrating multimodal content features and context features based on a self-supervised strategy. Extensive experiments on four real-world datasets demonstrate that IMMPOI achieves a 6% to 10% performance improvement compared to state-of-the-art methods.

The expressiveness of historical reviews in capturing user preferences has garnered significant attention in recommender systems. However, this technology still has certain limitations. Firstly, irrelevant reviews can introduce noise that may adversely affect the performance of the model. Secondly, existing approaches often assume a flat structure for review features, thus failing to capture the intricate and hierarchical nature of user-item interactions. Thirdly, it is challenging for review-based recommendation models to effectively assess the usefulness of reviews due to sparse supervision signals. To address these challenges, we propose a novel Hierarchical Gating and Discriminative model for rating prediction. Specifically, we introduce a local gating module that utilizes personalized end-to-end differential thresholds to select reviews in a relatively “hard” manner, thereby minimizing the impact of noisy reviews while facilitating model training. Additionally, we incorporate a global gating module to assess the overall usefulness of review signals by estimating the uncertainties inherent in historical reviews. Moreover, we propose a hierarchical discriminative network to develop self-supervision signals at both global and local levels to guide the learning of the hierarchical gating network. Extensive experiments on public datasets have demonstrated the effectiveness of the proposed model, and further investigations provide deep insight into its superiority.

In recent years, Graph Convolutional Networks (GCNs) have seen widespread utilization within micro-video recommendation systems, facilitating the understanding of user preferences through interactions with micro-videos. Despite the commendable performance exhibited by GCN-based methodologies, several persistent issues demand further scrutiny. Primarily, most user-micro-video interactions involve implicit behaviors, such as clicks or abstentions, which may inadvertently capture irrelevant micro-video content, thereby introducing significant noise (false touches, low watch-ratio, low ratings) into users’ histories. Consequently, this noise undermines the efficacy of micro-video recommendations. Moreover, the abundance of micro-videos has resulted in fewer interactions between users and micro-video content. To tackle these challenges, we propose a noise-resistant and anti-sparse graph learning framework for micro-video recommendation. Initially, we construct a denoiser that leverages implicit multi-attribute information (e.g., watch-ratio, timestamp, ratings, etc.) to filter noisy data from user interaction histories. This process yields high-fidelity micro-video information, enabling a more precise modeling of users’ feature preferences. Subsequently, we employ a multi-view reconstruction approach and utilize cross-view self-supervised learning to gain insights into user and micro-video features. This strategic approach effectively mitigates the issue of data sparsity. Extensive experiments conducted on two publicly available micro-video recommendation datasets validate the effectiveness of our proposed method. For in-depth details and access to the code, please refer to our repository at “ https://github.com/kbk12/ANAGL.git .”

Recent studies tend to employ Contrastive Learning (CL) methods to facilitate model training by extracting self-supervised signals to mitigate data sparsity. However, existing CL-based recommendation methods have not fully exploited the rich semantic information present in multi -modal data. To address these limitations, we propose a new CL-based recommendation framework named Multi-level Cross-modal Contrastive Learning (MCCL), which aims to construct multi-level contrastive learning to fully exploit the intra- and inter-modal semantic information in a self-supervised manner. Specifically, we innovatively consider user interaction and semantic review as two distinct semantic modalities, and devise two modal-specific contrastive learning strategies to enhance intra-modal learning. Furthermore, we leverage the semantic consistency between modalities to construct a multi-level cross-modal contrastive learning framework. Finally, a multi-task learning method is employed for collaborative optimization across multiple tasks. We verify the efficacy of MCCL via comprehensive experiments on three real-world datasets. MCCL achieves a significant performance improvement over the state-of-the-art baseline models.

Review-based recommendations suffer from text noises and the absence of supervised signals. To address those challenges, we propose a novel hierarchical gated sentiment-aware model for rating prediction in this paper. Specifically, to automatically suppress the influence of noisy reviews, we propose a hierarchical gating network to select informative textual signals at different levels of granularity. Specifically, a local gating module is proposed to select reviews with personalized end-to-end differential thresholds. A global gating module is employed to evaluate the overall usefulness of the review signals by estimating the uncertainties encoded in the historical reviews. In addition, a discriminative learning module is proposed to supervise the learning of the hierarchical gating network. The essential intuition is to exploit the sentiment consistencies between the target reviews and the target ratings for developing self-supervision signals. Finally, extensive experiments on public datasets and comparison studies with state-of-the-art baselines have demonstrated the effectiveness of the proposed model, additional investigations also provide a deep insight into the rationale underlying the superiority of the proposed model.

The considerable semantic information contained in multimodal data is increasingly appreciated by industry and academia. To effectively leverage multimodal information, existing multimodal recommendation methods mainly build multimodal auxiliary graphs to improve the representation of users and items. However, the weak value density of multimodal data inevitably leads to serious noise issues, making it difficult to effectively exploit valuable information from the multimodal contents. To address this issue, we propose a novel Fine-grained Self-supervised Mom entum Distillation Framework (FUMMER) for multimodal recommendations. Specifically, we propose a Transformer-based Fine-grained Feature Extractor (TFFE) and a Momentum Distillation (MoD) structure that incorporates intra- and inter-modal contrastive learning to fully pre-train TFFE for fine-grained feature extraction. Additionally, we design a structure-aware fine-grained contrastive learning module to fully exploit the self-supervised signals from fine-grained structural features. Extensive experiments on three real-world datasets show that our method outperforms state-of-the-art multimodal recommendation methods. Further experiments verify that the fine-grained feature extraction method we propose can serve as a pre-trained model, enhancing the performance of recommendation methods effectively by learning the fine-grained feature representations of items. The code is publicly available at https://github.com/BIAOBIAO12138/FUMMER.

In the past decade, various methods have been proposed to address document-level sentiment classification. However, the exploration of user–product interactions has not been sufficiently studied in the literature. In this work, we aim to investigate the effectiveness of exploiting user–product relations, and propose a hybrid semantic and interactive model for the classification task. The novelty of the proposed method is a ranking graph module and a latent matching module, where the former is capable of capturing high-order connectivity among the nodes, while the later is able to preserve semantics of local connectivity during the recursive graph learning processing. These two modules are seamlessly incorporated, enabling the proposed model to learn comprehensive and discriminative representations for the specific classification task. We conduct extensive experiments on three public datasets, and demonstrate the advantage of the proposed model over the state-of-the-art baselines.

Review-based recommendations suffer from text noises and the absence of supervised signals. To address those challenges, we propose a novel hierarchical gated sentiment-aware model for rating prediction in this paper. Specifically, to automatically suppress the influence of noisy reviews, we propose a hierarchical gating network to select informative textual signals at different levels of granularity. Specifically, a local gating module is proposed to select reviews with personalized end-to-end differential thresholds. The aim is to gate reviews in a relatively "hard" way to minimize the information flow from noisy reviews while facilitating the model training. A global gating module is employed to evaluate the overall usefulness of the review signals by estimating the uncertainties encoded in the historical reviews. In addition, a discriminative learning module is proposed to supervise the learning of the hierarchical gating network. The essential intuition is to exploit the sentiment consistencies between the target reviews and the target ratings for developing self-supervision signals so that the hierarchical gating network can select relevant reviews related to the target ratings for better prediction. Finally, extensive experiments on public datasets and comparison studies with state-of-the-art baselines have demonstrated the effectiveness of the proposed model, additional investigations also provide a deep insight into the rationale underlying the superiority of the proposed model.

The Sine Cosine Algorithm (SCA) is an outstanding optimizer that is appreciably used to dissolve complicated real-world problems. Nevertheless, this algorithm lacks sufficient population diversification and a sufficient balance between exploration and exploitation. So, effective techniques are required to tackle the SCA's fundamental shortcomings. Accordingly, the present paper suggests an improved version of SCA called Hierarchical Multi-Leadership SCA (HMLSCA) which uses an effective hierarchical multi-leadership search mechanism to lead the search process on multiple paths. The efficiency of the HMLSCA has been appraised and compared with a set of famous metaheuristic algorithms to dissolve the classical eighteen benchmark functions and thirty CEC 2017 test suites. The results demonstrate that the HMLSCA outperforms all compared algorithms and that the proposed algorithm provided a promising efficiency. Moreover, the HMLSCA was applied to handle the medicine data classification by optimizing the support vector machine's (SVM) parameters and feature weighting in eight datasets. The experiential outcomes verify the productivity of the HMLSCA with the highest classification accuracy and a gain scoring 1.00 Friedman mean rank versus the other evaluated metaheuristic algorithms. Furthermore, the proposed algorithm was used to diagnose COVID-19, in which it attained the topmost accuracy of 98% in diagnosing the infection on the COVID-19 dataset, which proves the performance of the proposed search strategy.

Review-based recommendations suffer from text noises and the absence of supervised signals. To address those challenges, we propose a novel hierarchical gated sentiment-aware model for rating prediction in this paper. Specifically, to automatically suppress the influence of noisy reviews, we propose a hierarchical gating network to select informative textual signals at different levels of granularity. Specifically, a local gating module is proposed to select reviews with personalized end-to-end differential thresholds. The aim is to gate reviews in a relatively ''hard" way to minimize the information flow from noisy reviews while facilitating the model training. A global gating module is employed to evaluate the overall usefulness of the review signals by estimating the uncertainties encoded in the historical reviews. In addition, a discriminative learning module is proposed to supervise the learning of the hierarchical gating network. The essential intuition is to exploit the sentiment consistencies between the target reviews and the target ratings for developing self-supervision signals so that the hierarchical gating network can select relevant reviews related to the target ratings for better prediction. Finally, extensive experiments on public datasets and comparison studies with state-of-the-art baselines have demonstrated the effectiveness of the proposed model, additional investigations also provide a deep insight into the rationale underlying the superiority of the proposed model.

Traditional recommender systems encounter several challenges such as data sparsity and unexplained recommendation. To address these challenges, many works propose to exploit semantic information from review data. However, these methods have two major limitations in terms of the way to model textual features and capture textual interaction. For textual modeling, they simply concatenate all the reviews of a user/item into a single review. However, feature extraction at word/phrase level can violate the meaning of the original reviews. As for textual interaction, they defer the interactions to the prediction layer, making them fail to capture complex correlations between users and items. To address those limitations, we propose a novel Hierarchical Text Interaction model (HTI) for rating prediction. In HTI, we propose to model low-level word semantics and high-level review representations hierarchically. The hierarchy allows us to exploit textual features at different granularities. To further capture complex user–item interactions, we propose to exploit semantic correlations between each user–item pair at different hierarchies. At word level, we propose an attention mechanism specialized to each user–item pair, and capture the important words for representing each review. At review level, we mutually propagate textual features between the user and item, and capture the informative reviews. The aggregated review representations are integrated into a collaborative filtering framework for rating prediction. Experiments on five real-world datasets demonstrate that HTI outperforms state-of-the-art models by a large margin. Further case studies provide a deep insight into HTI’s ability to capture semantic correlations at different levels of granularities for rating prediction.

Various aspects of human activity recognition have been researched so far and a variety of methods have been used to address them. Most of this research assumed that the data sources used for the recognition task are static. In real environments, however, sensors can be added or can fail and be replaced by different types of sensors. It is therefore important to create an activity recognition model that is able to leverage dynamically available sensors. To approach this problem, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. In our previous work, we proposed sensor and activity context models to address sensor heterogeneity and also a learning-to-rank method for activity learning and its adaptation based on the proposed context models. However, most of the existing solutions, including our previous work, require labelled data for training. To tackle this problem and further improve the recognition accuracy, in this paper, we propose a knowledge-based method for activity recognition and activity model adaptation with dynamically available contexts in an unsupervised manner. We also propose a semi-supervised data selection method for activity model adaptation, so the activity model can be adapted without labelled data. We use comprehensive datasets to demonstrate effectiveness of the proposed methods, and show their advantage over the conventional machine learning algorithms in terms of recognition accuracy.

Unlike traditional video recommendations, micro-video inherits the characteristics of social platforms, such as social relation. A large amount of micro-videos showing explosive growth is badly affecting the user's choice. In this paper, we propose a multi-source multi-net micro-video recommendation model that recommends micro-videos fitting users' best interests. Different from existing works, as micro-video inherits the characteristics of social platforms, we simultaneously incorporate multi-source content data of items and multi-networks of users to learn user and item representations for recommendation. This information can be complementary to each other in a way that multi-modality data can bridge the semantic gap among items, while multi-type user networks, such as following and reposting, are able to propagate the preferences among users. Furthermore, to discover the hidden categories of micro-videos that properly match users' interests, we interactively learn the user–item representations and perform the hidden item category clustering. The resulted categorical representations are interacted with user representations to model user preferences at different levels of hierarchies. Finally, multi-source content item data, multi-type user networks and hidden item categories are jointly modelled in a unified recommender, and the parameters of the model are collaboratively learned to boost the recommendation performance. Experiments on a real dataset demonstrate the effectiveness of the proposed model and its advantage over the state-of-the-art baselines.