Article
Computer Science, Information Systems
Vidyanand Choudhary, Zhe (James) Zhang
Summary: We study an online environment where a firm provides product recommendations to consumers. The firm makes personalized recommendations based on its uncertainty about consumer preferences. We find that the firm's recommendation bias, profit, and consumer surplus are influenced by the interaction between the firm's uncertainty and consumer search costs.
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jie Wang, Jingya Zhou, Zhen Wu, Xigang Sun
Summary: This paper introduces a multi-attention aware recommendation method, MARec, which improves the feature representation of entities in Heterogeneous Information Networks (HIN) by designing and selecting meta-paths. It also incorporates recent trend changes using a Bi-LSTM and compensation mechanism, and captures the importance of different papers/users through an adaptive fusion of their interactive historical data.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Dac Huu Nguyen, Son Thanh Huynh, Cuong Viet Dinh, Phong Tan Huynh, Binh Thanh Nguyen
Summary: This paper investigates the submission recommendation system for computer science and applied mathematics and proposes an efficient recommendation algorithm that outperforms other methods. The research results are significant for scientists and publishers.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Agyemang Paul, Zhefu Wu, Kai Liu, Shufeng Gong
Summary: Information retrieval is useful in all aspects of life. Many systems use personalized recommendation to assist users, but traditional methods only model user-product interaction data, yielding limited results. This paper proposes a personalized recommendation method that combines visual, temporal, and sequential information, and demonstrates its effectiveness through experiments.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Editorial Material
Multidisciplinary Sciences
Amanda Heidt
Summary: Developers aim to assist scientists in drawing connections from a vast amount of literature, enabling them to concentrate on discovery and innovation.
Article
Computer Science, Information Systems
Arpita Chaudhuri, Monalisa Sarma, Debasis Samanta
Summary: The extraneous growth of scientific information over the Internet has made it difficult for researchers to find relevant papers among millions of research papers. Existing research paper recommendation systems have limitations in exploiting prominent information of papers and do not consider a sound ranking strategy. This study proposes a systematic hidden attribute-based recommendation engine (SHARE) that utilizes multiple hidden features to provide valuable insights of papers and a novel ranking strategy to retrieve personalized and important papers.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Hongwu Qin, Meng Zhao, Xiuqin Ma, HuanLing Sun, Weiyi Wei
Summary: This paper presents a new model called Best Matching Collaborator Recommendation (BMCR) to help scholars find suitable collaborators based on their academic level. Experimental results demonstrate that our model improves the feasibility of cooperation and achieves significant improvements in precision rate, recall rate, and F1 score.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yi Li, Ronghui Wang, Guofang Nan, Dahui Li, Minqiang Li
Summary: This study introduces a personalized paper recommendation method based on a heterogeneous network, which utilizes meta-paths and random walks to capture user preferences and improve recommendation accuracy. Results from experiments using different datasets show that the proposed method outperforms other baseline methods.
DECISION SUPPORT SYSTEMS
(2021)
Article
Mathematics
Peisen Yuan, Yi Sun, Hengliang Wang
Summary: Recommendation systems are widely used on the internet to predict user preferences through interactions with products, utilizing heterogeneous information networks to address data challenges and search for metapaths suitable for different recommendation tasks.
Article
Automation & Control Systems
Hailong Chen, Hao Wu, Jiahui Li, Xin Wang, Lei Zhang
Summary: Developers need to reuse web services and create mashups suitable for various scenarios. However, inexperienced developers may not be able to adequately express their requirements when using service recommendation systems, leading to inappropriate recommendations. To address this, a service-keyword correlation graph (SKCG) is defined to capture the relationship between services and keywords. A keyword-based deep reinforced Steiner tree search (K-DRSTS) approach is then proposed, which models the task of service discovery as a Steiner tree search problem and uses deep reinforcement learning to provide an efficient solution. Experimental results on real-world data sets demonstrate the effectiveness of K-DRSTS.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Xia Xiao, Junyan Xu, Jiaying Huang, Chengde Zhang, Xinzhong Chen
Summary: This paper proposes an innovative approach to address the issue of distinguishing authors' research interests caused by sparse binary interactions. It introduces a ternary coauthor recommendation method that utilizes an academic heterogeneous graph and a multilayer perceptron for enriching author-paper interactions, thus enhancing paper recommendation performance.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xin Mei, Xiaoyan Cai, Sen Xu, Wenjie Li, Shirui Pan, Libin Yang
Summary: This paper discusses the cold start problem in research paper recommendation and proposes a new method that considers network structure, textual information, and co-authorship of papers. A mutual reinforcement network embedding model is developed to enhance the recommendation. Experimental results demonstrate the effectiveness of the proposed method.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematics, Interdisciplinary Applications
Jie Chen, Xin Wang, Shu Zhao, Yanping Zhang
Summary: This paper proposes a content-enhanced network embedding model for academic collaborator recommendation, which builds a content-enhanced academic collaborator network based on weighted text representation, capturing the latent semantic relationships among researchers.
Proceedings Paper
Computer Science, Artificial Intelligence
Jie Yu, Junchen He, Lingyu Xu
Summary: This paper proposes a hypergraph-based academic paper recommendation method, which constructs a hypergraph to model the complex relationship between scholars and papers, and achieve multi-features fusion. Additionally, a light hypergraph-based collaborative filtering algorithm is proposed to mine high-order similarity and provide trusted recommendations.
KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, KSEM 2022, PT III
(2022)
Article
Computer Science, Artificial Intelligence
Jingyuan Wang, Ning Wu, Wayne Xin Zhao
Summary: This paper studies Personalized Route Recommendation (PRR) in location-based services, proposing a principled solution by improving search algorithms with neural networks. Experimental results demonstrate the effectiveness and robustness of the proposed model on multiple real-world datasets.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Information Systems
Yanxiang Ling, Fei Cai, Jun Liu, Honghui Chen, Maarten de Rijke
Summary: Recent research emphasizes the importance of mixed-initiative interactions in conversational search. The task of question generation (QG) in open-domain conversational systems aims to enhance human-machine interactions. However, the limited availability of QG-specific data in conversations makes this task challenging. In this study, we propose a context-enhanced neural question generation (CNQG) model that leverages conversational context to predict question content and pattern. We also use multi-task learning with auxiliary training objectives and a self-supervised approach to train our question generator.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Economics
Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
Summary: Probabilistic time series forecasting is crucial in various domains, and Transformer-based methods have achieved state-of-the-art performance. However, they require a large number of parameters and high memory requirements. To address this, we propose a novel bidirectional temporal convolutional network with significantly fewer parameters. Our method performs on par with state-of-the-art approaches and requires lower memory, reducing infrastructure cost.
INTERNATIONAL JOURNAL OF FORECASTING
(2023)
Article
Computer Science, Information Systems
Romain Deffayet, Jean-Michel Renders, Maarten De Rijke
Summary: The performance of click models under policy distributional shift (PDS) is examined, and a new evaluation protocol is proposed to predict their performance under PDS, along with guidelines to mitigate risks.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Jun Ma, Maarten De Rijke
Summary: One of the key challenges in sequential recommendation is how to extract and represent user preferences. We propose a transformer-based sequential recommendation model, named MrTransformer, to explore multiple user preferences. MrTransformer employs preference-editing-based self-supervised learning mechanism to disentangle user preferences into multiple independent representations, improving preference extraction and representation. Experiments show that MrTransformer with preference editing outperforms state-of-the-art methods in terms of Recall, MRR, and NDCG, especially for long sequences of interactions.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Chen Wu, Ruqing Zhang, Jiafeng Guo, Maarten De Rijke, Yixing Fan, Xueqi Cheng
Summary: This article introduces the Word Substitution Ranking Attack (WSRA) task against Neural Ranking Models (NRMs), which aims to promote a target document's ranking by adding adversarial perturbations to its text. The proposed Pseudo Relevance-based ADversarial ranking Attack (PRADA) method outperforms existing attack strategies and successfully fools the NRM with small indiscernible perturbations of text.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Ming Li, Sami Jullien, Mozhdeh Ariannezhad, Maarten De Rijke
Summary: The study aims to investigate the performance of NBR methods in practical applications and proposes a new set of evaluation metrics to measure the performance of NBR models. By conducting experimental analysis on state-of-the-art NBR models, it reveals the actual progress and improvements of NBR methods in the recommendation process.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wenchao Sun, Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke
Summary: This study addresses the challenges of sequential recommendation in a context where multiple users share a single account and behavior is available in multiple domains. The proposed PSJNet network learns role-specific representations and filters out irrelevant information using a gating mechanism. It also combines split and join techniques to learn cross-domain representations. Experimental results demonstrate that PSJNet outperforms state-of-the-art baselines in terms of MRR and Recall.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Ericmoore Ngharamike, Li-Minn Ang, Kah Phooi Seng, Mingzhong Wang
Summary: The electric network frequency (ENF) is a fluctuating signal representing the frequency of mains power system. This fluctuation can be utilized to extract information about the source camera of a video recorded under ENF-affected lighting. By considering the ENF and the camera-specific read-out time (T-ro), the suggested approach in this paper aims to identify the source camera of an unknown video. Experimental results demonstrate the effectiveness of the proposed method.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Yanxiang Ling, Fei Cai, Jun Liu, Honghui Chen, Maarten de Rijke
Summary: Hierarchical context modeling is crucial for the response generation in multi-turn conversational systems. We propose a model named KS-CQ, which utilizes the Keep and Select modules to generate neighbor-aware context representation and context-enriched query representation. Extensive experiments demonstrate the effectiveness of our approach compared to state-of-the-art baselines in both automatic and human evaluations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Proceedings Paper
Computer Science, Information Systems
Philipp Hager, Maarten de Rijke, Onno Zoeter
Summary: Inverse-propensity scoring and neural click models are compared in this study for learning rankers from user clicks affected by position bias. Theoretical differences are explored and empirical comparisons are conducted on a prevalent evaluation setup. It is shown that both methods optimize for true document relevance when position bias is known, but small empirical differences are found when neural click models learn from shared, conflicting features.
ADVANCES IN INFORMATION RETRIEVAL, ECIR 2023, PT I
(2023)
Proceedings Paper
Computer Science, Information Systems
Mariya Hendriksen, Svitlana Vakulenko, Ernst Kuiper, Maarten de Rijke
Summary: This article investigates the reproducibility and replicability of state-of-the-art CMR results when evaluated on object-centric and scene-centric datasets. By selecting two different architectures of CMR models and evaluating them on two scene-centric datasets and three object-centric datasets, it is discovered that the reproducibility and replicability of the experimental results are problematic, and the scores obtained by the models on object-centric datasets are significantly lower than those obtained on scene-centric datasets.
ADVANCES IN INFORMATION RETRIEVAL, ECIR 2023, PT III
(2023)
Proceedings Paper
Computer Science, Information Systems
Thilina C. Rajapakse, Maarten de Rijke
Summary: Dense retrieval methods have outperformed traditional sparse retrieval methods in open-domain retrieval. However, there is a noticeable decrease in accuracy when these methods are applied to out-of-distribution and out-of-domain datasets. This may be due to the mismatch in information available to the context encoder and the query encoder during training. By training on datasets with multiple queries per passage, we show that dense passage retriever models perform better on out-of-distribution and out-of-domain test datasets compared to models trained on datasets with single query per passage.
ADVANCES IN INFORMATION RETRIEVAL, ECIR 2023, PT II
(2023)
Article
Computer Science, Artificial Intelligence
Xin Li, Haojie Lei, Li Zhang, Mingzhong Wang
Summary: This paper explores interpretable Deep Reinforcement Learning (DRL) by representing policy using Differentiable Inductive Logic Programming (DILP). The research focuses on the optimization perspective of DILP-based policy learning and proposes using Mirror Descent for policy optimization. The theoretical and empirical studies verify the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Proceedings Paper
Computer Science, Information Systems
Ajinkya Kale, Surya Kallumadi, Tracy Holloway King, Shervin Malmasi, Maarten de Rijke, Jacopo Tagliabue
Summary: eCommerce Information Retrieval is gaining attention in the academic literature and is essential for major eCommerce websites. The workshop aims to bring together researchers and practitioners to discuss unique topics in eCommerce IR and explore ways to improve search relevance using the combination of free text, structured data, and customer behavioral data.
PROCEEDINGS OF THE 45TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '22)
(2022)
Proceedings Paper
Computer Science, Information Systems
Mozhdeh Ariannezhad, Sami Jullien, Ming Li, Min Fang, Sebastian Schelter, Maarten de Rijke
Summary: This paper presents an empirical study on the repeat consumption behavior of users in the context of grocery shopping. The study highlights the significance of repeat purchases in the performance of next basket recommendation (NBR). To address this, the authors propose ReCANet, a neural network model that explicitly models the repeat consumption behavior of users, and demonstrate its superior performance compared to state-of-the-art models for the NBR task.
PROCEEDINGS OF THE 45TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '22)
(2022)