An Efficient Deep Learning Model to Infer User Demographic Information From Ratings

Liu, Yongsheng; Qu, Hong; Chen, Wenyu; Mahmud, Sakib

doi:10.1109/access.2019.2911720

Cited by 9 publications

(8 citation statements)

References 23 publications

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“…[136], whereas forecasting mobile density in the city was done by Liang et al [137] applying DL to CDR data. Similarly, CDR data has been used to forecast visitors' future places of visit [138] and predict users' gender and age [139]. All these studies have collectively acknowledged that wireless network issues can be addressed better using ML techniques than traditional approaches, but they remain a challenge.…”

Section: Network Data Analysismentioning

confidence: 99%

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

Samanta¹,

Sadhukhan²,

Samaddar³

et al. 2022

CAAI Trans on Intel Tech

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The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users. Due to this popularity, there has been a huge rise in mobile data volume, applications, types of services, and number of customers. Furthermore, due to the COVID‐19 pandemic, the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home. This massive increase in demand for multi‐class services has posed numerous challenges to wireless network frameworks. The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic, such as real‐time live streaming of videos, audios, text, images etc., at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers. Next‐generation wireless networks (NGWNs, i.e. 5G networks and beyond) are being developed to accommodate the service qualities mentioned above and many more. However, achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers. It requires the analysis of a huge volume of network data (structured and unstructured) received or collected from heterogeneous devices, applications, services, and customers and the effective and dynamic management of network parameters based on this analysis in real time. In the ever‐increasing network heterogeneity and complexity, machine learning (ML) techniques may become an efficient tool for effectively managing these issues. In recent days, the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain. This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.

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Section: Network Data Analysismentioning

confidence: 99%

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

Samanta¹,

Sadhukhan²,

Samaddar³

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

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“…The RMSE result for this algorithm is %0.9874. Liu et al [31] introduced a method called Deep Retentive learning to predict demographic information using MovieLens dataset. They used an automatic feature selection technique instead of a handcrafted method.…”

Section: Related Workmentioning

confidence: 99%

The Empirical Comparison of the Supervised Classifiers Performances in Implementing a Recommender System using Various Computational Platforms

Mohammadi¹

2020

IJISA

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Recommender Systems (RS) help users in making appropriate decisions. In the area of RS research, many researchers focused on improving the performances of the existing methods, but most of them have not considered the potential of their employed methods in reaching the ultimate solution. In our view, the Machine Learning supervised approach as one of the existing techniques to create an RS can reach higher degrees of success in this field. Thus, we implemented a Collaborative Filtering recommender system using various Machine Learning supervised classifiers to study their performances. These classifiers implemented not only on a traditional platform but also on the Apache Spark platforms. The Caret package is used to implement the algorithms in the classical computational platform, and the H2O and Sparklyr are used to run the algorithms on the Spark Machine. Accordingly, we compared the performance of our algorithms with each other and with other algorithms from recent literature. Our experiments indicate the Caret-based algorithms are significantly slower than the Sparklyr and H2O based algorithms. Also, in the Spark platform, the runtime of the Sparklyr-based algorithm decreases with increasing the cluster size. However, the H2O-based algorithms run slower with increasing the cluster size. Moreover, the comparison of the results of our implemented algorithms with each other and with other algorithms from recent literature shows the Bayesian network is the fastest classifier between our implemented classifiers, and the Gradient Boost Model is the most accurate algorithm in our research. Therefore, the supervised approach is better than the other methods to create a collaborative filtering recommender system.

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“…Among different CF methods, Matrix Factorization (MF) is one of the more popular ones due to its scalability and its flexibility in incorporating additional information; cf, for instance, [4], [5], [8]. User side-information, such as may be extracted via social networks [9], [10], user demographics [11], [12] or reviews [13], [14], has been widely used to incorporate into MF and proven being helpful at improving the recommendation performance. However, for natural personal and privacy rights and reasons, users are usually resistant to having their personal information accessed by a third-party agent or to spending extra time on supplying their opinions (i.e.…”

Section: Introductionmentioning

confidence: 99%

FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation

et al. 2021

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Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large useritem rating matrix. However, when the rating matrix sparseness increases its performance deteriorates. Expanding MF to include side-information of users and items has been shown by many both to improve general recommendation performance and to help alleviate the sparsity and cold-start issues in CF. In regard to item feature side-information, most schemes incorporate this information through a two stage process: intermediate results (e.g., on item similarity) are first computed based on item attributes; these are then combined with MF. In this paper, focussing on item side-information, we propose a model that directly incorporates item features with the MF framework in a single step process. The model, which we name FeatureMF, does this by projecting every available attribute datum in each of the item features into the same latent factor space with users and items, thereby in effect enriching item representation in the MF model. Results are presented of comparative performance experiments of the model against three state-ofthe-art item information enriched models, as well as against four reference benchmark models, using two public real-world datasets, Douban and Yelp, with four training:test ratio scenarios each. It is shown to yield the best recommendation performance over all these models across all contexts including data sparsity situations, in particular, achieving over 0.9% to over 6.5% MAE recommendation performance improvement over the next best model, HERec. The model is also found to alleviate cold start and to scale well, almost linearly, in regard to computational time, as a function of dataset size.

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An Efficient Deep Learning Model to Infer User Demographic Information From Ratings

Cited by 9 publications

References 23 publications

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

The Empirical Comparison of the Supervised Classifiers Performances in Implementing a Recommender System using Various Computational Platforms

FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation

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