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Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting Artical

Longbing Cao

Engineering 2016, Volume 2, Issue 2,   Pages 212-224 doi: 10.1016/J.ENG.2016.02.013

Abstract:

While recommendation plays an increasingly critical role in our living, study, work, and entertainmentIn this paper, the non-IID nature and characteristics of recommendation are discussed, followed by thetheoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendationThis non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research

Keywords： relationship     Coupling learning     Relational learning     IIDness learning     Non-IIDness learning     Recommender system     Recommendation     Non-IID recommendation    

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Toward Privacy-Preserving Personalized Recommendation Services Review

Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren

Engineering 2018, Volume 4, Issue 1,   Pages 21-28 doi: 10.1016/j.eng.2018.02.005

Abstract:

Recommendation systems are crucially important for the delivery of personalized services to users.With personalized recommendation services, users can enjoy a variety of targeted recommendations suchIn addition, personalized recommendation services have become extremely effective revenue drivers forWe present the general architecture of personalized recommendation systems, the privacy issues therein, and existing works that focus on privacy-preserving personalized recommendation services.

Keywords： Privacy protection     Personalized recommendation services     Targeted delivery     Collaborative filtering     Machine    

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Rare tumors: a blue ocean of investigation

Frontiers of Medicine 2023, Volume 17, Issue 2,   Pages 220-230 doi: 10.1007/s11684-023-0984-z

Abstract: Lastly, we pinpointed the current recommendation chance for patients with rare tumors to be involved

Keywords： rare tumors     diagnosis flowchart     treatment strategy     clinical trials recommendation    

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A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Zhen-ming YUAN,Chi HUANG,Xiao-yan SUN,Xing-xing LI,Dong-rong XU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 7,   Pages 532-540 doi: 10.1631/FITEE.1400368

Abstract: In this paper, we propose a collaborative filtering recommendation algorithm based on a temporal interestA questionnaire survey proved user satisfaction with recommendation results when the cold-start problem

Keywords： Recommender system     Collaborative filtering     Social tagging     Interest evolution model    

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EncyCatalogRec: catalog recommendation for encyclopedia article completion Article

Wei-ming LU, Jia-hui LIU, Wei XU, Peng WANG, Bao-gang WEI

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 436-447 doi: 10.1631/FITEE.1800363

Abstract: So, the recommendation problem is changed to a transductive learning problem in the product graph.Experimental results demonstrate that our approach achieves state-of-the-art performance on catalog recommendation

Keywords： Catalog recommendation     Encyclopedia article completion     Product graph     Transductive learning    

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Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1273-1286 doi: 10.1631/FITEE.2200304

Abstract: Next point-of-interest (POI) recommendation is an important personalized task in location-based social

Keywords： Point-of-interest recommendation     Spatiotemporal effects     Long short-term memory (LSTM)     Attention mechanism    

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DAN: a deep association neural network approach for personalization recommendation Research Articles

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-980 doi: 10.1631/FITEE.1900236

Abstract: The collaborative filtering technology used in traditional systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional algorithms, thus leading to the emergence of systems based on . At present, s mostly use deep s to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the . Aimed at this problem, in this paper we propose a feedforward deep method, called the deep association (DAN), which is based on the joint action of multiple categories of information, for implicit feedback . Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint s can provide better performance.

Keywords： Neural network     Deep learning     Deep association neural network (DAN)     Recommendation    

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Fast code recommendation via approximate sub-tree matching Research Article

Yichao SHAO, Zhiqiu HUANG, Weiwei LI, Yaoshen YU,shaoyichao@nuaa.edu.cn,zqhuang@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1205-1216 doi: 10.1631/FITEE.2100379

Abstract: Software developers often write code that has similar functionality to existing code segments. A tool that helps developers reuse these code fragments can significantly improve their efficiency. Several methods have been proposed in recent years. Some use sequence matching algorithms to find the related recommendations. Most of these methods are time-consuming and can leverage only low-level textual information from code. Others extract features from code and obtain similarity using numerical feature vectors. However, the similarity of feature vectors is often not equivalent to the original code’s similarity. Structural information is lost during the process of transforming abstract syntax trees into vectors. We propose an approximate sub-tree matching based method to solve this problem. Unlike existing tree-based approaches that match feature vectors, it retains the tree structure of the query code in the matching process to find code fragments that best match the current query. It uses a fast approximation sub-tree matching algorithm by transforming the sub-tree matching problem into the match between the tree and the list. In this way, the structural information can be used for tasks that have high time requirements. We have constructed several real-world code databases covering different languages and granularities to evaluate the effectiveness of our method. The results show that our method outperforms two compared methods, SENSORY and Aroma, in terms of the recall value on all the datasets, and can be applied to large datasets.

Keywords： Code reuse     Code recommendation     Tree similarity     Structure information    

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APFD: an effective approach to taxi route recommendation with mobile trajectory big data Research Article

Wenyong ZHANG, Dawen XIA, Guoyan CHANG, Yang HU, Yujia HUO, Fujian FENG, Yantao LI, Huaqing LI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 10,   Pages 1494-1510 doi: 10.1631/FITEE.2100530

Abstract:

With the rapid development of data-driven intelligent transportation systems, an efficient method for taxis has become a hot topic in smart cities. We present an effective taxi approach (called APFD) based on the (APF) method and method with mobile trajectory big data. Specifically, to improve the efficiency of , we propose a method that searches for a region including the optimal route through the origin and destination coordinates. Then, based on the APF method, we put forward an effective approach for removing redundant nodes. Finally, we employ the method to determine the optimal . In particular, the APFD approach is applied to a simulation map and the real-world road network on the Fourth Ring Road in Beijing. On the map, we randomly select 20 pairs of origin and destination coordinates and use APFD with the ant colony (AC) algorithm, greedy algorithm (A∗), APF, rapid-exploration random tree (RRT), non-dominated sorting genetic algorithm-II (NSGA-II), particle swarm optimization (PSO), and for the shortest . Compared with AC, A∗, APF, RRT, NSGA-II, and PSO, concerning shortest route planning, APFD improves route planning capability by 1.45%–39.56%, 4.64%–54.75%, 8.59%–37.25%, 5.06%–45.34%, 0.94%–20.40%, and 2.43%–38.31%, respectively. Compared with , the performance of APFD is improved by 1.03–27.75 times in terms of the execution efficiency. In addition, in the real-world road network, on the Fourth Ring Road in Beijing, the ability of APFD to recommend the shortest route is better than those of AC, A∗, APF, RRT, NSGA-II, and PSO, and the execution efficiency of APFD is higher than that of the method.

Keywords： Big data analytics     Region extraction     Artificial potential field     Dijkstra     Route recommendation     GPS    

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RepoLike: amulti-feature-based personalized recommendation approach for open-source repositories None

Cheng YANG, Qiang FAN, Tao WANG, Gang YIN, Xun-hui ZHANG, Yue YU, Hua-min WANG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 2,   Pages 222-237 doi: 10.1631/FITEE.1700196

Abstract:

With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness, a large number of external contributors have been attracted to the open-source communities. They are playing a significant role in open-source development. However, the open-source development online is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks from thousands of open-source projects in the communities to work on. In this paper, we propose a new approach called “RepoLike,” to recommend repositories for developers based on linear combination and learning to rank. It uses the project popularity, technical dependencies among projects, and social connections among developers to measure the correlations between a developer and the given projects. Experimental results show that our approach can achieve over 25% of hit ratio when recommending 20 candidates, meaning that it can recommend closely correlated repositories to social developers.

Keywords： Social coding     Open-source software     Personal recommendation     GitHub    

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A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation Research Article

Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN,11521043@zju.edu.cn,yinzh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1416-1429 doi: 10.1631/FITEE.2200662

Abstract: (TCM) is an interesting research topic in China’s thousands of years of history. With the recent advances in artificial intelligence technology, some researchers have started to focus on learning the TCM prescriptions in a data-driven manner. This involves appropriately recommending a set of herbs based on patients’ symptoms. Most existing models disregard TCM domain knowledge, for example, the interactions between symptoms and herbs and the TCM-informed observations (i.e., TCM formulation of prescriptions). In this paper, we propose a knowledge-guided and TCM-informed approach for . The knowledge used includes path interactions and co-occurrence relationships among symptoms and herbs from a generated from TCM literature and prescriptions. The aforementioned knowledge is used to obtain the discriminative feature vectors of symptoms and herbs via a . To increase the ability of herb prediction for the given symptoms, we introduce TCM-informed observations in the prediction layer. We apply our proposed model on a TCM prescription dataset, demonstrating significant improvements over state-of-the-art methods.

Keywords： Traditional Chinese medicine     Herb recommendation     Knowledge graph     Graph attention network    

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Phosphorus supply and management in vegetable production systems in China

Rui WANG, Weiming SHI, Yilin LI

Frontiers of Agricultural Science and Engineering 2019, Volume 6, Issue 4,   Pages 348-356 doi: 10.15302/J-FASE-2019277

Abstract:

Vegetable production systems involve high rates of chemical and organic fertilizer applications, leading to significant P accumulation in vegetable soils, as well as a decrease in P use efficiency (PUE), which is one of the key limiting factors in vegetable production. This review introduces the vegetable production systems in China and their fertilization status, and analyzes probable causes of overfertilization of vegetable fields. Poorly developed root systems and high P demand have led to the need to maintain much higher available P concentrations in the root zone for regular growth of vegetables, which might necessitate higher phosphate fertilizer input than the plants require. Research on strategies to improve vegetable PUE and the mechanisms of these strategies are summarized in this review. Increasing the P uptake by vegetables by supplying P during the critical growth stage and effectively utilizing the accumulated P by optimizing the C:P ratio in soils can substantially increase PUE. These advances will provide a basis for improving PUE and optimizing phosphate fertilizer applications in vegetable production through regulatory measures. In addition, some policies are recommended that could ensure the safety of vegetables and improve product quality. This review also aims to improve understanding of P cycling in vegetable fields and assist in the development of best practices to manage P reserves globally.

Keywords： phosphate fertilizer     phosphorus use efficiency     vegetable production systems     phosphorus management     policy recommendation    

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Explainable data transformation recommendation for automatic visualization Research Article

Ziliang WU, Wei CHEN, Yuxin MA, Tong XU, Fan YAN, Lei LV, Zhonghao QIAN, Jiazhi XIA,wzlzju@zju.edu.cn,chenvis@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 7,   Pages 1007-1027 doi: 10.1631/FITEE.2200409

Abstract: To tackle these challenges, we propose a novel explainable recommendation approach for extended kindsA recommendation algorithm is designed to compute optimal transformations, which can reveal specified

Keywords： Data transformation     Data transformation recommendation     Automatic visualization     Explainability    

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Self-supervised graph learning with target-adaptive masking for session-based recommendation Research Article

Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 73-87 doi: 10.1631/FITEE.2200137

Abstract: aims to predict the next item based on a user's limited interactions within a short period. Existing approaches use mainly recurrent neural networks (RNNs) or (GNNs) to model the sequential patterns or the transition relationships between items. However, such models either ignore the over-smoothing issue of GNNs, or directly use cross-entropy loss with a softmax layer for model optimization, which easily results in the over-fitting problem. To tackle the above issues, we propose a self-supervised graph learning with (SGL-TM) method. Specifically, we first construct a global graph based on all involved sessions and subsequently capture the self-supervised signals from the global connections between items, which helps supervise the model in generating accurate representations of items in the ongoing session. After that, we calculate the main supervised loss by comparing the ground truth with the predicted scores of items adjusted by our designed module. Finally, we combine the main supervised component with the auxiliary self-supervision module to obtain the final loss for optimizing the model parameters. Extensive experimental results from two benchmark datasets, Gowalla and Diginetica, indicate that SGL-TM can outperform state-of-the-art baselines in terms of Recall@20 and MRR@20, especially in short sessions.

Keywords： Session-based recommendation     Self-supervised learning     Graph neural networks     Target-adaptive masking    

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A self-supervised method for treatment recommendation in sepsis Research Articles

Sihan Zhu, Jian Pu,jianpu@fudan.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7,   Pages 926-939 doi: 10.1631/FITEE.2000127

Abstract: treatment is a highly challenging effort to reduce mortality in hospital intensive care units since the treatment response may vary for each patient. Tailored s are desired to assist doctors in making decisions efficiently and accurately. In this work, we apply a self-supervised method based on (RL) for on individuals. An uncertainty evaluation method is proposed to separate patient samples into two domains according to their responses to treatments and the state value of the chosen policy. Examples of two domains are then reconstructed with an auxiliary transfer learning task. A distillation method of privilege learning is tied to a variational auto-encoder framework for the transfer learning task between the low- and high-quality domains. Combined with the self-supervised way for better state and action representations, we propose a deep RL method called high-risk uncertainty (HRU) control to provide flexibility on the trade-off between the effectiveness and accuracy of ambiguous samples and to reduce the expected mortality. Experiments on the large-scale publicly available real-world dataset MIMIC-III demonstrate that our model reduces the estimated mortality rate by up to 2.3% in total, and that the estimated mortality rate in the majority of cases is reduced to 9.5%.

Keywords： 治疗推荐；脓毒症；自监督学习；强化学习；电子病历    

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Title Author Date Type Operation

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

Longbing Cao

Journal Article

Toward Privacy-Preserving Personalized Recommendation Services

Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren

Journal Article

Rare tumors: a blue ocean of investigation

Journal Article

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Zhen-ming YUAN,Chi HUANG,Xiao-yan SUN,Xing-xing LI,Dong-rong XU

Journal Article

EncyCatalogRec: catalog recommendation for encyclopedia article completion

Wei-ming LU, Jia-hui LIU, Wei XU, Peng WANG, Bao-gang WEI

Journal Article

Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Journal Article

DAN: a deep association neural network approach for personalization recommendation

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Journal Article

Fast code recommendation via approximate sub-tree matching

Yichao SHAO, Zhiqiu HUANG, Weiwei LI, Yaoshen YU,shaoyichao@nuaa.edu.cn,zqhuang@nuaa.edu.cn

Journal Article

APFD: an effective approach to taxi route recommendation with mobile trajectory big data

Wenyong ZHANG, Dawen XIA, Guoyan CHANG, Yang HU, Yujia HUO, Fujian FENG, Yantao LI, Huaqing LI

Journal Article

RepoLike: amulti-feature-based personalized recommendation approach for open-source repositories

Cheng YANG, Qiang FAN, Tao WANG, Gang YIN, Xun-hui ZHANG, Yue YU, Hua-min WANG

Journal Article

A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation

Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN,11521043@zju.edu.cn,yinzh@zju.edu.cn

Journal Article

Phosphorus supply and management in vegetable production systems in China

Rui WANG, Weiming SHI, Yilin LI

Journal Article

Explainable data transformation recommendation for automatic visualization

Ziliang WU, Wei CHEN, Yuxin MA, Tong XU, Fan YAN, Lei LV, Zhonghao QIAN, Jiazhi XIA,wzlzju@zju.edu.cn,chenvis@zju.edu.cn

Journal Article

Self-supervised graph learning with target-adaptive masking for session-based recommendation

Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn

Journal Article

A self-supervised method for treatment recommendation in sepsis

Sihan Zhu, Jian Pu,jianpu@fudan.edu.cn

Journal Article