In large-scale Point-of-Interest (POI) recommendation, the conflict between accuracy and computational efficiency intensifies as POI catalogs grow. Traditional deep models struggle to balance quality with efficiency. To address this challenge, we propose a knowledge-distilled product quantization framework EffiPOI for efficient POI recommendation. EffiPOI jointly optimizes accuracy and efficiency by integrating product quantization with multi-modal knowledge distillation. Specifically, we first construct service-oriented multi-modal POI representations, which comprehensively capture each POI’s spatial coverage, temporal activity patterns, and semantic attributes. Based on these representations, we design a teacher-student distillation paradigm. The teacher model adopts a Mixture-of-Experts architecture to generate discriminative and semantically expressive POI representations, which serve as high-quality supervision signals for guiding the student model through knowledge distillation. The student model leverages product quantization to encode POIs into compact and computation-friendly representations, achieving a favorable tradeoff between representational compactness and predictive accuracy. To alleviate the performance degradation due to quantization, we develop a hybrid knowledge distillation strategy that transfers both response-aware and feature-aware knowledge from the teacher model to the student model. Experimental results on three real-world datasets show that the proposed method achieves 4.6%–12.1% improvements in accuracy and over 10× speedup in inference efficiency, outperforming existing POI recommendation models. Code is available at: https://github.com/pcm1217/EffiPOI .

Providing sequential recommendations along with easily comprehensible natural language explanations can significantly enhance users’ trust in the recommender systems. However, this approach presents two key challenges: (1) The different objectives of the two tasks make it challenging to achieve joint optimization and mutual enhancement. (2) The simultaneous generation of accurate sequential recommendations and high-quality natural language explanations presents serious challenges to the model’s time and space efficiency. To address these challenges, we propose a general and efficient multi-task self-prompt framework for explainable sequential recommendation (ARTS), which improves collaboration performance and time and space efficiency of multi-task modules based on the generated personalized semantic prompts. Specifically, we propose a self-prompt generator that transfers the user’s global behavior features into the continuous prompt, achieving efficient information sharing among multi-task modules. Additionally, we design a personalized prompt-based short sequence inputs strategy under the pre-training and prompt-tuning paradigm, which achieves mutual enhancement among the multi-task modules and significantly improves the model’s time and space efficiency. Extensive experiments have verified that the proposed ARTS outperforms the state-of-the-art methods in both sequential recommendation and explanation generation tasks. The generality, efficiency and effectiveness of each module of the framework have also been validated through various experiments 1 .

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.

Diversity is increasingly recognized as a crucial factor in recommendation systems for enhancing user satisfaction. However, existing studies on diversity-enhanced recommendation systems primarily focus on designing recommendation strategies, often overlooking the development of evaluation metrics. Widely used diversity metrics such as CC, ILAD, and ILMD are typically assessed independently of accuracy. This separation leads to a critical limitation: existing diversity measures are unable to distinguish between diversity improvements from effective recommendations and those from in effective recommendations. Our evaluations reveal that the diversity improvements are primarily contributed by ineffective recommendations, which often do not positively contribute to user satisfaction. Furthermore, existing diversity metrics disregard the feature distribution of ground-truth items, potentially skewing the assessment of diversity performance. To address these limitations, we design three new accuracy-aware metrics: DCC, FDCC, and DILAD, and conduct a re-evaluation using these metrics. Surprisingly, our results illustrate that the diversity improvements of existing diversity-enhanced approaches are limited and even negative compared to those of accurate recommendations. This finding underscores the need to explore more sophisticated diversity-enhanced techniques for improving the diversity within effective recommendations.

The Point-of-Interest (POI) recommendation system, designed to recommend potential future visits of users based on their check-in sequences, faces the challenge of data scarcity. This challenge primarily stems from the data sparsity issue, namely users interact with only a small number of POIs. Most existing studies attempt to solve this problem by focusing on POI check-in sequences, without considering the substantial multi-modal content information (e.g. textual and image data) commonly associated with POIs. In this paper, we propose a novel multi-modal content-aware framework for POI recommendation (MMPOI). Our approach addresses the issue of data sparsity by incorporating multi-modal content information about POIs from a new perspective. Specifically, MMPOI leverages pre-trained models for inter-modal conversion and employs a unified pre-trained model to extract modal-specific features from each modality, effectively bridging the semantic gap between different modalities. We propose to build a Multi-Modal Trajectory Flow Graph (MTFG) which combines the multi-modal semantic structure with check-in sequences. Moreover, we design an adaptive multi-task Transformer that models users' multi-modal movement patterns and integrates them for the next POI recommendation tasks. Extensive experiments on four real-world datasets demonstrate that MMPOI outperforms state-of-the-art POI recommendation methods. To facilitate reproducibility, we have released both the code and the multi-modal POI recommendation datasets we collect https://github.com/zzmylq/MMPOI

Hashing-based recommender systems that represent users and items as binary hash codes are recently proposed to significantly improve time and space efficiency. However, the highly developed social media presents two major challenges to hashing-based recommendation algorithms. Firstly, the boundary between information producers and consumers becomes blurred, resulting in the rapid emergence of massive online content. Meanwhile, users' limited information consumption capacity inevitably causes further interaction sparsity. The inherent high sparsity of data leads to insufficient hash learning. Secondly, a considerable amount of online content becomes fast-moving consumer goods, such as short videos and news commentary, causing frequent changes in user interests and item popularity. To address the above problems, we propose a Temporal Social Graph Network Hashing (TSGNH) method for efficient recommendation, which generates binary hash codes of users and items through dynamic-adaptive aggregation on a constructed temporal social graph network. Specifically, we build a temporal social graph network to fully capture the social information widely existing in practical recommendation scenarios and propose a dynamic-adaptive aggregation method to capture long-term and short-term characters of users and items. Furthermore, different from the discrete optimization approaches used by existing hashing-based recommendation methods, we devise an end-to-end hashing learning approach that incorporates balanced and de-correlated constraints to learn compact and informative binary hash codes tailored for recommendation scenarios. Extensive experiments on three widely evaluated recommendation datasets demonstrate the superiority of the proposed method.

Hashing is an effective technique to improve the efficiency of large-scale recommender system by representing both users and items into binary codes. However, existing hashing-based recommendation methods still suffer from two important problems: 1) Cold-start. They employ the user-item interactions and single auxiliary information to learn the binary hash codes. But the full interaction history is not always available and the single auxiliary information may be missing. 2) Efficient optimization. They learn the hash codes with two-step relaxed optimization or one-step discrete hash optimization based on the cyclic coordinate descent, which results in significant quantization loss or still consumes considerable computation time. In this paper, we propose a Multi-modal Discrete Collaborative Filtering (MDCF) for efficient cold-start recommendation. We map the multi-modal features of users and items to a consensus Hamming space based on the matrix factorization framework. Specifically, a low-rank self-weighted multi-modal fusion module is designed to adaptively fuse the multi-modal features into binary hash codes. Additionally, to support large-scale recommendation, a fast discrete optimization method based on augmented Lagrangian multiplier is developed to directly compute the binary hash codes with simple operations. Experiments show the superior performance of the proposed method over state-of-the-art baselines.

Using hashing to learn the binary codes of users and items significantly improves the efficiency and reduces the space consumption of the recommender system. However, existing hashing-based recommender systems remain black boxes without any explainable outputs that illustrate why the system recommends the items. In this paper, we present a new end-to-end discrete recommendation framework based on the multi-task learning to simultaneously perform explainable and efficient recommendation. Toward this goal, an Explainable Discrete Collaborative Filtering (EDCF) method is proposed to preserve the user-item interaction features and semantic text features into binary hash codes by adaptively exploiting the correlations between the preference prediction task and the explanation generation task. At the online recommendation stage, EDCF makes efficient top-K recommendation by calculating the Hamming distances between the feature hash codes, and simultaneously generates natural language explanations for recommendation results through the explanation generation module. To obtain the hash codes directly from the end-to-end neural network, we introduce an attentive TextCNN and an Adaptive Tanh layer in the preference prediction task. For explanation generation, Long Short-Term Memory is employed to generate the explanations for recommendation results from the binary hash codes of user and item. Experiments demonstrate the superiority of the proposed method.