Cross Domain Recommender Systems

Khan, Muhammad Murad; Ibrahim, Roliana; Ghani, Imran

doi:10.1145/3073565

Cited by 109 publications

(21 citation statements)

References 101 publications

(106 reference statements)

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“…Apart from the gaps already mentioned in the above section, the study of Khan & Ibrahim [86] also pointed out future research directions for cross domain recommender systems. Those can be arranged into five groups, namely domain similarity enhancement, algorithm improvement, using of "big data" as a source domain, conventional recommender systems problems, and dataset extensions.…”

Section: Challenges and Open Issuesmentioning

confidence: 97%

“…The study of Khan & Ibrahim [86] did a particularly good job at summarizing the literature in Cross Domain Recommendation Systems. The objective of this study was to identify the widely used CDRS building-block definition and to classify and visualize current CDRS research in the frame of identified building-block definitions.…”

Section: Transfer Learning In Recommender Systems 221 Summary Of LImentioning

confidence: 99%

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Transfer Learning in Recommender Systems

Yang

Dai²,

Pan

2020

Transfer Learning

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Section: Challenges and Open Issuesmentioning

confidence: 97%

Section: Transfer Learning In Recommender Systems 221 Summary Of LImentioning

confidence: 99%

Transfer Learning in Recommender Systems

Yang

Dai²,

Pan

2020

Transfer Learning

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“…Nowadays, users have multiple accounts on social networks. The data from multiple sites assist RS in reducing the cold-start problem [46][47][48][49][50][51]. Authors in [46] have worked in this direction to integrate cross-domain data.…”

Section: B Cross-domainmentioning

confidence: 99%

“…In another work, [22] use multi-view neural networks and propose a cross-domain RS to overcome the data sparsity problem. Recently, Khan et al [51] give a broad survey of cross-domain RSs.…”

Section: B Cross-domainmentioning

confidence: 99%

“…Moreover, deep learning has also opened the doors for improving the accuracy of social recommender systems. [19], [37], [44], [46], [52] 2016 17 [16], [21], [24], [25], [28], [36], [40], [41], [45], [47], [57], [60], [61], [69], [70], [65], [71] 2017 20 [17], [18], [20], [22], [26], [27], [38], [42], [43], [51], [53], [62]- [64], [66], [68], [72]- [74], [78] VI. CONCLUSION A voluminous research has been done and is also proceeding in recommender systems using deep learning.…”

Section: Miscellaneousmentioning

confidence: 99%

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An Application-oriented Review of Deep Learning in Recommender Systems

Shokeen¹,

Rana²

2019

IJISA

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The development in technology has gifted huge set of alternatives. In the modern era, it is difficult to select relevant items and information from the large amount of available data. Recommender systems have been proved helpful in choosing relevant items. Several algorithms for recommender systems have been proposed in previous years. But recommender systems implementing these algorithms suffer from various challenges. Deep learning is proved successful in speech recognition, image processing and object detection. In recent years, deep learning has been also proved effective in handling information overload and recommending items. This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications. The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.

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Interval time series forecasting: A systematic literature review

Wang,

Gurmani,

Tao

et al. 2023

Journal of Forecasting

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Interval time series forecasting can be used for forecasting special symbolic data comprising lower and upper bounds and plays an important role in handling the complexity, instability, and uncertainty of observed objects. The purpose of this research is to identify the most widely used definition of interval time series; classify existing research into mature research, current research focus, and research gaps within the defined framework; and recommend future directions for interval forecasting research. To achieve this goal, we have conducted a systematic literature review, comprising search strategy planning, screening mechanism determination, document analysis, and report generation. During the search strategy planning stage, eight literature search libraries are selected to obtain the most extensive studies (total of 525 targets). In the screening‐mechanism determination stage, through the inclusion and exclusion mechanism, the literature that is repetitive, of low‐relevance, and from other fields are discarded, and 125 studies are finally selected. In the document analysis stage, tag‐based methods and classification grids are selected to analyze the shortlisted studies. The results show that there are still numerous research gaps in interval time series forecasting, such as the establishment of hybrid models, application of multisource information, development and application of evaluation techniques, and expansion of application scenarios. In the report‐generation stage, the problems that have been solved and encountered in interval forecasting are summarized, and future research directions are proposed. Finally, the most significant contribution of this research is to provide an overview of interval time series forecasting for easy reference by researchers and to facilitate further research.

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Cross Domain Recommender Systems

Cited by 109 publications

References 101 publications

Transfer Learning in Recommender Systems

Transfer Learning in Recommender Systems

An Application-oriented Review of Deep Learning in Recommender Systems

Interval time series forecasting: A systematic literature review

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