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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 entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID) in existing theories and systems. Another phenomenon is that, while tremendous efforts have been made to model specific aspects of users or items, the overall user and item characteristics and their non-IIDness have been overlooked. In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneity. This non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. It creates exciting new directions and fundamental solutions to address various complexities including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues.

Keywords： Independent and identically distributed (IID)     Non-IID     Heterogeneity     Coupling relationship     Coupling learning     Relational learning     IIDness learning     Non-IIDness learning     Recommender system     Recommendation     Non-IID recommendation    

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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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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 networks (LBSNs) and aims to recommend the next POI for users in a specific situation with historical check-in data. State-of-the-art studies linearly discretize the user’s spatiotemporal information and then use recurrent neural network (RNN) based models for modeling. However, these studies ignore the nonlinear effects of spatiotemporal information on user preferences and spatiotemporal correlations between user trajectories and candidate POIs. To address these limitations, a spatiotemporal trajectory (STT) model is proposed in this paper. We use the long short-term memory (LSTM) model with an attention mechanism as the basic framework and introduce the user’s spatiotemporal information into the model in encoding. In the process of encoding information, an exponential decay factor is applied to reflect the nonlinear drift of user interest over time and distance. In addition, we design a spatiotemporal matching module in the process of recalling the target to select the most relevant POI by measuring the relevance between the user’s current trajectory and the candidate set. We evaluate the performance of our STT model with four real-world datasets. Experimental results show that our model outperforms existing state-of-the-art methods.

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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Cohort-based personalized query auto-completion Regular Papers-Research Articles

Dan-yang JIANG, Hong-hui CHEN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 9,   Pages 1246-1258 doi: 10.1631/FITEE.1800010

Abstract: Query auto-completion (QAC) facilitates query formulation by predicting completions for given query prefix inputs. Most web search engines use behavioral signals to customize query completion lists for users. To be effective, such personalized QAC models rely on the access to sufficient context about each user’s interest and intentions. Hence, they often suffer from data sparseness problems. For this reason, we propose the construction and application of cohorts to address context sparsity and to enhance QAC personalization. We build an individual’s interest profile by learning his/her topic preferences through topic models and then aggregate users who share similar profiles. As conventional topic models are unable to automatically learn cohorts, we propose two cohort topic models that handle topic modeling and cohort discovery in the same framework. We present four cohortbased personalized QAC models that employ four different cohort discovery strategies. Our proposals use cohorts’ contextual information together with query frequency to rank completions. We perform extensive experiments on the publicly available AOL query log and compare the ranking effectiveness with that of models that discard cohort contexts. Experimental results suggest that our cohort-based personalized QAC models can solve the sparseness problem and yield significant relevance improvement over competitive baselines.

Keywords： Query auto-completion     Cohort-based retrieval     Topic models    

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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 such as movies, books, ads, restaurants, and more. In addition, personalized recommendation services have become extremely effective revenue drivers for online business. Despite the great benefits, deploying personalized recommendation services typically requires the collection of users’ personal data for processing and analytics, which undesirably makes users susceptible to serious privacy violation issues. Therefore, it is of paramount importance to develop practical privacy-preserving techniques to maintain the intelligence of personalized recommendation services while respecting user privacy. In this paper, we provide a comprehensive survey of the literature related to personalized recommendation services with privacy protection. We present the general architecture of personalized recommendation systems, the privacy issues therein, and existing works that focus on privacy-preserving personalized recommendation services. We classify the existing works according to their underlying techniques for personalized recommendation and privacy protection, and thoroughly discuss and compare their merits and demerits, especially in terms of privacy and recommendation accuracy. We also identity some future research directions.

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

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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: Online encyclopedias such as Wikipedia provide a large and growing number of articles on many topics. However, the content of many articles is still far from complete. In this paper, we propose EncyCatalogRec, a system to help generate a more comprehensive article by recommending catalogs. First, we represent articles and catalog items as embedding vectors, and obtain similar articles via the locality sensitive hashing technology, where the items of these articles are considered as the candidate items. Then a relation graph is built from the articles and the candidate items. This is further transformed into a product graph. So, the recommendation problem is changed to a transductive learning problem in the product graph. Finally, the recommended items are sorted by the learning-to-rank technology. Experimental results demonstrate that our approach achieves state-of-the-art performance on catalog recommendation in both warm- and cold-start scenarios. We have validated our approach by a case study.

Keywords： Catalog recommendation     Encyclopedia article completion     Product graph     Transductive learning    

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A novel confidence estimation method for heterogeneous implicit feedback Article

Jing WANG, Lan-fen LIN, Heng ZHANG, Jia-qi TU, Peng-hua YU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1817-1827 doi: 10.1631/FITEE.1601468

Abstract: Implicit feedback, which indirectly reflects opinion through user behaviors, has gained increasing attention in rec-ommender system communities due to its accessibility and richness in real-world applications. A major way of exploiting implicit feedback is to treat the data as an indication of positive and negative preferences associated with vastly varying confidence levels. Such algorithms assume that the numerical value of implicit feedback, such as time of watching, indicates confidence, rather than degree of preference, and a larger value indicates a higher confidence, although this works only when just one type of implicit feedback is available. However, in real-world applications, there are usually various types of implicit feedback, which can be referred to as heterogeneous implicit feedback. Existing methods cannot efficiently infer confidence levels from heterogeneous implicit feedback. In this paper, we propose a novel confidence estimation approach to infer the confidence level of user prefer-ence based on heterogeneous implicit feedback. Then we apply the inferred confidence to both point-wise and pair-wise matrix factorization models, and propose a more generic strategy to select effective training samples for pair-wise methods. Experiments on real-world e-commerce datasets from Tmall.com show that our methods outperform the state-of-the-art approaches, consid-ering several commonly used ranking-oriented evaluation criteria.

Keywords： Recommender systems     Heterogeneous implicit feedback     Confidence     Collaborative filtering     E-commerce    

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Finite-time coordinated path-following control of leader-following multi-agent systems Research Article

Weibin CHEN, Yangyang CHEN, Ya ZHANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 10,   Pages 1511-1521 doi: 10.1631/FITEE.2100476

Abstract:

This paper presents applications of the continuous feedback method to achieve path-following and a formation moving along the desired orbits within a finite time. It is assumed that the topology for the virtual leader and followers is directed. An additional condition of the so-called is designed to make all agents move within a limited area. A novel continuous path-following control law is first designed based on the and backstepping. Then a novel continuous formation algorithm is designed by regarding the path-following errors as disturbances. The settling-time properties of the resulting system are studied in detail and simulations are presented to validate the proposed strategies.

Keywords： Finite-time     Coordinated path-following     Multi-agent systems     Barrier function    

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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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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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Preference transfer model in collaborative filtering for implicit data Project supported by the National Basic Research Program (973) of China (No. 2012CB316400) and the National Natural Science Foundation of China (No. 61571393) Article

Bin JU,Yun-tao QIAN,Min-chao YE

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 6,   Pages 489-500 doi: 10.1631/FITEE.1500313

Abstract: Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users’ buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized. Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then, two factor-user matrices can be used to construct a so-called ‘preference dictionary’ that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.

Keywords： Recommender systems     Collaborative filtering     Preference transfer model     Cross domain     Implicit data    

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A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering Article

Yong-ping DU, Chang-qing YAO, Shu-hua HUO, Jing-xuan LIU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 5,   Pages 658-666 doi: 10.1631/FITEE.1601732

Abstract: The collaborative filtering (CF) technique has been widely used recently in recommendation systems. It needs historical data to give predictions. However, the data sparsity problem still exists. We propose a new item-based restricted Boltzmann machine (RBM) approach for CF and use the deep multilayer RBM network structure, which alleviates the data sparsity problem and has excellent ability to extract features. Each item is treated as a single RBM, and different items share the same weights and biases. The parameters are learned layer by layer in the deep network. The batch gradient descent algorithm with minibatch is used to increase the convergence speed. The new feature vector discovered by the multilayer RBM network structure is very effective in predicting a rating and achieves a better result. Experimental results on the data set of MovieLens show that the item-based multilayer RBM approach achieves the best performance, with a mean absolute error of 0.6424 and a root-mean-square error of 0.7843.

Keywords： Restricted Boltzmann machine     Deep network structure     Collaborative filtering     Recommendation system    

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Finding map regions with high density of query keywords Article

Zhi YU, Can WANG, Jia-jun BU, Xia HU, Zhe WANG, Jia-he JIN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1543-1555 doi: 10.1631/FITEE.1600043

Abstract: We consider the problem of finding map regions that best match query keywords. This region search problem can be applied in many practical scenarios such as shopping recommendation, searching for tourist attractions, and collision region detection for wireless sensor networks. While conventional map search retrieves isolate locations in a map, users frequently attempt to find regions of interest instead, e.g., detecting regions having too many wireless sensors to avoid collision, or finding shopping areas featuring various merchandise or tourist attractions of different styles. Finding regions of interest in a map is a non-trivial problem and retrieving regions of arbitrary shapes poses particular challenges. In this paper, we present a novel region search algorithm, dense region search (DRS), and its extensions, to find regions of interest by estimating the density of locations containing the query keywords in the region. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of our algorithm.

Keywords： Map search     Region search     Region recommendation     Spatial keyword search     Geographic information system     Location-based service    

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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

A self-supervised method for treatment recommendation in sepsis

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

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

Cohort-based personalized query auto-completion

Dan-yang JIANG, Hong-hui CHEN

Journal Article

Toward Privacy-Preserving Personalized Recommendation Services

Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren

Journal Article

EncyCatalogRec: catalog recommendation for encyclopedia article completion

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

Journal Article

A novel confidence estimation method for heterogeneous implicit feedback

Jing WANG, Lan-fen LIN, Heng ZHANG, Jia-qi TU, Peng-hua YU

Journal Article

Finite-time coordinated path-following control of leader-following multi-agent systems

Weibin CHEN, Yangyang CHEN, Ya ZHANG

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

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

Preference transfer model in collaborative filtering for implicit data Project supported by the National Basic Research Program (973) of China (No. 2012CB316400) and the National Natural Science Foundation of China (No. 61571393)

Bin JU,Yun-tao QIAN,Min-chao YE

Journal Article

A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering

Yong-ping DU, Chang-qing YAO, Shu-hua HUO, Jing-xuan LIU

Journal Article

Finding map regions with high density of query keywords

Zhi YU, Can WANG, Jia-jun BU, Xia HU, Zhe WANG, Jia-he JIN

Journal Article