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Bowei Chen

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5 papers

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EAAI Journal 2026 Journal Article

Dimos: Diffusion model with unified sequential state space for session-based recommendation

Weiyue Li
Ming Gao
Bowei Chen
Jingmin An
Hao Dong
Wei Jiang
Jiafu Tang

Session-based recommendation aims to predict the next item based on the user–item interactions within the current session. Many existing methods adopt discriminative approaches to learn specific preference representations, while few methods introduce generative approaches to learn underlying preference distributions, failing to handle limited and noisy interactions effectively. Moreover, naive implementations of generative models face a trade-off between effectiveness and efficiency, limiting their practical utility. To address these challenges, we propose Dimos, a dual-branch framework comprising an exploring branch and an exploiting branch, which leverage diffusion models and attention networks to capture implicit and explicit preferences, respectively. At the core of Dimos is Bi-MaKAN, a novel backbone architecture featuring a pair of parameter-sharing bidirectional Mamba blocks and a Kolmogorov–Arnold network-based feature fusion layer, designed to enhance both performance and efficiency. To further improve generalization and reduce overfitting, we unify the sequential state spaces of both branches. Additionally, we introduce a linearly weighted fusion mechanism that integrates preference representations from both branches, enabling flexible adjustment of implicit and explicit preference contributions during training and inference. Extensive experiments on three real-world benchmark datasets demonstrate the superiority of Dimos, achieving up to 2. 79% improvement in Recall, 3. 09% in Mean Reciprocal Rank (MRR), and 3. 00% in Normalized Discounted Cumulative Gain (NDCG) over state-of-the-art baselines. Efficiency evaluations show substantial gains, with reductions of 94. 32% in Graphics Processing Unit (GPU) memory usage, 66. 81% in training time, and 98. 80% in inference time. In-depth analyses reveal a collaborative effect between the two branches during both training and inference, with dataset scale modulating their relative importance.

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IS Journal 2021 Journal Article

Adversarial Path Sampling for Recommender Systems

Rui Ding
Bowei Chen
Guibing Guo
Xiaochun Yang

Generative adversarial networks (GANs) have achieved a big success in collaborative filtering (CF). However, existing GAN-based methods in CF still suffer from the high-sparsity and cold-start problems; in addition, they also undergo the issues of excessive space complexity or inadequate training. In this article, we propose path2rec a novel adversarial path-based recommendation model to address these limitations of existing GAN-based methods in recommendation task by naturally incorporating auxiliary information (e. g. , social networks and item attributes). It is composed of two modules, 1) pathGAN and 2) path2vec. In pathGAN, we consider both explicit and implicit friends, as well as item attributes by regarding them as the source of graph construction. Then, we propose a smart walk strategy to automatically generate an optimizing path, which can effectively learn the semantic distribution of users and items. In path2vec, to fully exploit context features of the generated path, we use the Continuous Bag of Words (CBOW) model to fine-tune nodes representations learned by pathGAN. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of the proposed path2rec by applying it into top- n item recommendation, which reaches better performance than other counterparts.

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AAAI Conference 2020 Conference Paper

Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach

Yu Zheng
Bowei Chen
Timothy M. Hospedales
Yongxin Yang

Partial (replication) index tracking is a popular passive investment strategy. It aims to replicate the performance of a given index by constructing a tracking portfolio which contains some constituents of the index. The tracking error optimisation is quadratic and NP-hard when taking the 0 constraint into account so it is usually solved by heuristic methods such as evolutionary algorithms. This paper introduces a simple, efﬁcient and scalable connectionist model as an alternative. We propose a novel reparametrisation method and then solve the optimisation problem with stochastic neural networks. The proposed approach is examined with S&P 500 index data for more than 10 years and compared with widely used index tracking approaches such as forward and backward selection and the largest market capitalisation methods. The empirical results show our model achieves excellent performance. Compared with the benchmarked models, our model has the lowest tracking error, across a range of portfolio sizes. Meanwhile it offers comparable performance to the others on secondary criteria such as volatility, Sharpe ratio and maximum drawdown.

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AAAI Conference 2020 Conference Paper

Leveraging Title-Abstract Attentive Semantics for Paper Recommendation

Guibing Guo
Bowei Chen
Xiaoyan Zhang
Zhirong Liu
Zhenhua Dong
Xiuqiang He

Paper recommendation is a research topic to provide users with personalized papers of interest. However, most existing approaches equally treat title and abstract as the input to learn the representation of a paper, ignoring their semantic relationship. In this paper, we regard the abstract as a sequence of sentences, and propose a two-level attentive neural network to capture: (1) the ability of each word within a sentence to reﬂect if it is semantically close to the words within the title. (2) the extent of each sentence in the abstract relative to the title, which is often a good summarization of the abstract document. Speciﬁcally, we propose a Long-Short Term Memory (LSTM) network with attention to learn the representation of sentences, and integrate a Gated Recurrent Unit (GRU) network with a memory network to learn the long-term sequential sentence patterns of interacted papers for both user and item (paper) modeling. We conduct extensive experiments on two real datasets, and show that our approach outperforms other state-of-the-art approaches in terms of accuracy.

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TIST Journal 2015 Journal Article

Multi-Keyword Multi-Click Advertisement Option Contracts for Sponsored Search

Bowei Chen
Jun Wang
Ingemar J. Cox
Mohan S. Kankanhalli

In sponsored search, advertisement (abbreviated ad) slots are usually sold by a search engine to an advertiser through an auction mechanism in which advertisers bid on keywords. In theory, auction mechanisms have many desirable economic properties. However, keyword auctions have a number of limitations including: the uncertainty in payment prices for advertisers; the volatility in the search engine’s revenue; and the weak loyalty between advertiser and search engine. In this article, we propose a special ad option that alleviates these problems. In our proposal, an advertiser can purchase an option from a search engine in advance by paying an upfront fee, known as the option price. The advertiser then has the right, but no obligation, to purchase among the prespecified set of keywords at the fixed cost-per-clicks (CPCs) for a specified number of clicks in a specified period of time. The proposed option is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keyword) and is also multi-exercisable (multi-click). This novel structure has many benefits: advertisers can have reduced uncertainty in advertising; the search engine can improve the advertisers’ loyalty as well as obtain a stable and increased expected revenue over time. Since the proposed ad option can be implemented in conjunction with the existing keyword auctions, the option price and corresponding fixed CPCs must be set such that there is no arbitrage between the two markets. Option pricing methods are discussed and our experimental results validate the development. Compared to keyword auctions, a search engine can have an increased expected revenue by selling an ad option.

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