Special Issue "Context-Aware Computing, Learning, and Big Data in Internet of Things"
Deadline for manuscript submissions: 31 March 2023 | Viewed by 11
2. School of NUOVOS, Ajeenkya D Y Patil University, Pune 412105, Maharashtra, India
Interests: detection system; network security; intrusion detection system; machine learning
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Hybrid context-aware recommender systems currently constitute a vital field of research. The wide development of mobile applications has provided a considerable number of diverse data. Machine learning plays a critical role in building collaborative context-aware recommender systems (CCARSs) able to predict item ratings or rankings. The overfitting of historical data in machine learning techniques presents a significant problem that can reduce the predictive power of CCARS. In order to avoid data overfitting, regularization terms should be implemented. It is important to fine-tune machine learning algorithms to avoid overfitting as much as possible. Algorithms based on dimensionality reduction could present a solution. Matrix factorization, which uses a denser representation of the user rating matrix, is one of the ways in which the existing collaborative filtering approaches can be improved. One of the main challenges in machine learning approaches is the fine-tuning of the hyperparameters; creating hypotheses from the available data can help in this task. Another possible solution is achieved through combining different recommender systems into one recommender system, known as a hybrid recommender system. Recommender systems operate based on collaborative filtering or content-based filtering, each of which has its own strengths and weaknesses. Combining different recommender system algorithms, resulting in hybrid algorithms, can take full advantage of the strengths of each component algorithm.
Recommender systems are also applied in the fields of big data and the Internet of things. In these areas, extracting information from big data and small data plays a prominent role in evaluating the performance of recommender systems.
This Special Issue will address the abovementioned challenges in data collection and processing in the Internet of things and provide relevant recommendations. The extraction of useful insights from available big data will also addressed. We invite submissions detailing innovative ideas and interesting research challenges.
Topics of interest include, but are not limited to:
- The issues in recommendation learning;
- The challenges existing in data collection and processing in the field of Internet of things;
- Extracting useful insights from the big data;
- Development of context-aware learning and recommender systems.
Prof. Dr. Vijayakumar Varadarajan
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- computational intelligence
- recommender systems
- Internet of things
- big data analytics and pattern recognition