Author name cluster

Yiheng Jiang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2026 Conference Paper

NP-MiSR: Neural Process-based Multi-Interest Learning for Session-Based Recommendation

Jun Bao
Junbo Wang
Yiheng Jiang
Xiangfeng Liu
Mingyang Lv
Yuanbo Xu

Session-based recommendation (SBR) aims to provide users with satisfactory suggestions via modeling preferences based on short-term, anonymous user-item interaction sequences. Traditional single interest learning methods struggle to align with the diverse nature of preferences. Recent advances resolved this bottleneck by learning multiple interest embeddings for each session. However, due to the pre-defining scheme of interest quantity (e.g. the number of interests), these approaches are deficient in adaptive ability towards distinctive preference patterns across different users. Moreover, these methods rely solely on the current session and ignore useful information from related ones. The short-term property of sessions would magnify the insufficient representation issue. To address these limitations, we propose a Neural Process-based Multi-interest learning framework for Session-based Recommendation, namely NP-MiSR. To be specific, our method enables adaptive multi-interest representation learning through two complementary mechanisms: 1) Neural Process-based Intra-session interest modeling: We employ Neural Processes to model the distribution of interests within a session, where the fixed interest configurations are no longer needed. 2) Cross-session context fusion: We extract interest distributions of similar sessions as contextual priors to refine the current session’s interest representation. Extensive experiments on three datasets demonstrate that our method consistently outperforms state-of-the-art SBR approaches with an average improvement of 38.8%. Moreover, the few-shot learning task reveals that NP-MiSR achieves a surprisingly favorable efficiency v.s. performance trade-off where utilizing only 10% of the training data attains 95% of the recommendation performance.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Auto Encoding Neural Process for Multi-interest Recommendation

Yiheng Jiang
Yuanbo Xu
Yongjian Yang
Funing Yang
Pengyang Wang
Chaozhuo Li

Multi-interest recommendation constantly aspires to an oracle individual preference modeling approach, that satisfies the diverse and dynamic properties. Fueled by the deep learning technology, existing neural network (NN)-based recommender systems employ single-point or multi-point interest representation strategy to realize preference modeling,and boost the recommendation performance with a remarkable margin. However, as parameterized approximate functions, NN-based methods remain deficiencies with respect to the adaptability towards distinctive preference patterns cross different users and the calibration over the individual current intent. In this paper, we revisit multi-interest recommendation with the lens of stochastic process and Bayesian inference. Specifically, we propose to learn a distribution over functions to depict the individual diverse preferences rather than a unified function to approximate preference. Subsequently, the recommendation is encouraged with the uncertainty estimation which conforms to the dynamic shifting intent. Along these lines, we establish the connection between multi-interest recommendation and neural processes by proposing NP-Rec, which realizes the flexible multiple interests modeling and uncertainty estimation, simultaneously. Empirical study on 4 real world datasets demonstrates that our NP-Rec attains superior recommendation performances to several state-of-the-art baselines, where the average improvement achieves up to 13.94%.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

Where and When: Predict Next POI and Its Explicit Timestamp in Sequential Recommendation

Yuanbo Xu
Hongxu Shen
Yiheng Jiang
En Wang

Sequential point-of-interest (POI) recommendation aims to recommend the next POI for users in accordance with their historical check-in information. However, few attempts treat timestamps of check-ins as a core factor for sequence models, leading to insufficient insight into user behavior and subsequently suboptimal recommendations. To address these limitations, we propose to assign equal importance to both POIs and their timestamps, shifting the point of view to recommend the next POI and predict the corresponding timestamp. Along these lines, we present the Time-Aware POI Recommender with Timestamp Prediction (TAPT), a multi-task learning framework for explainable POI recommendations. Specifically, we begin by decoupling timestamps into multi-dimensional vectors and propose a timestamp encoding module to explicitly encode these vectors. Additionally, we design a specialized timestamp prediction module built on the traditional sequence-based POI recommender backbone, effectively learning the strong correlation between POIs and their corresponding timestamps through these two modules. We evaluated the proposed model with three real-world LBSN datasets and demonstrated that TAPT achieves comparable or superior performance in POI recommendation compared to the baseline backbone. Besides, TAPT can not only recommend the next POI, but predict the corresponding timestamp in the future.

PDF Details DOI