Social media marketing through time‐aware collaborative filtering

Maio, Carmen De; Fenza, Giuseppe; Gallo, Mariacristina; Loia, Vincenzo; Parente, Mimmo

doi:10.1002/cpe.4098

Cited by 17 publications

(9 citation statements)

References 53 publications

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“…Fig. 7 is a Recall-Precision Graph according to the number of prediction places (3,5,7,9). According to the average result of comparison with the conventional method, performance is improved, as shown in Table 6.…”

Section: Methodsmentioning

confidence: 97%

“…Precision is the percentage of the restaurant that the user actually visited among the predicted restaurants, as shown in equation (6). Recall is the percentage of the restaurant that the predicted restaurant among the restaurants which is user actually visited, as shown in equation (7). F-measure is calculated as equation (8) with Precision and Recall.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, they are widely adopted in diverse field such as business, genetics, transportation, and distribution. Yet they have been perhaps most widely adopted in marketing [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Rating and Comments Mining Using TF-IDF and SO-PMI for Improved Priority Ratings

Kim¹,

Moon²

2019

KSII TIIS

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Data mining technology is frequently used in identifying the intention of users over a variety of information contexts. Since relevant terms are mainly hidden in text data, it is necessary to extract them. Quantification is required in order to interpret user preference in association with other structured data. This paper proposes rating and comments mining to identify user priority and obtain improved ratings. Structured data (location and rating) and unstructured data (comments) are collected and priority is derived by analyzing statistics and employing TF-IDF. In addition, the improved ratings are generated by applying priority categories based on materialized ratings through Sentiment-Oriented Point-wise Mutual Information (SO-PMI)-based emotion analysis. In this paper, an experiment was carried out by collecting ratings and comments on "place" and by applying them. We confirmed that the proposed mining method is 1.2 times better than the conventional methods that do not reflect priorities and that the performance is improved to almost 2 times when the number to be predicted is small.

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Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

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Rating and Comments Mining Using TF-IDF and SO-PMI for Improved Priority Ratings

Kim¹,

Moon²

2019

KSII TIIS

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“…For instance, Maioetal et al [33] promoted that the timeaware collaborative filtering can be adopted to evaluate what the users might hold interest on when they browse through Twitter. Text analysis service is adopted in this approach to annotate the content of Tweets semantically to track the notions based on the frequencies of being posted and forwarded along the time.…”

Section: Related Workmentioning

confidence: 99%

Information filtering in evolving online networks

Chen

Zhang

et al. 2018

Physics Letters A

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Recommender systems use the records of users' activities and profiles of both users and products to predict users' preferences in the future. Considerable works towards recommendation algorithms have been published to solve the problems such as accuracy, diversity, congestion, cold-start, novelty, coverage and so on. However, most of these research did not consider the temporal effects of the information included in the users' historical data. For example, the segmentation of the training set and test set was completely random, which was entirely different from the real scenario in recommender systems. More seriously, all the objects are treated as the same, regardless of the new, the popular or obsoleted products, so do the users. These data processing methods always lose useful information and mislead the understanding of the system's state. In this paper, we detailed analyzed the difference of the network structure between the traditional random division method and the temporal division method on two benchmark data sets, Netflix and MovieLens. Then three classical recommendation algorithms, Global Ranking method, Collaborative Filtering and Mass Diffusion method, were employed. The results show that all these algorithms became worse in all four key indicators, ranking score, precision, popularity and diversity, in the temporal scenario. Finally, we design a new recommendation algorithm based on both users' and objects' first appearance time in the system. Experimental results showed that the new algorithm can greatly improve the accuracy and other metrics.

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“…De Maio et al in the first paper “ Social Media Marketing through Time‐Aware Collaborative Filtering ,” define a time‐aware collaborative filtering for estimating users' interest along the time in Twitter. Their approach uses text analysis services to semantically annotate tweets' content and to track concepts considering post frequencies along the time.…”

mentioning

confidence: 99%

Special issue on advanced techniques for cloud data storage and collaborative systems

Xhafa

2017

Concurrency and Computation

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Cloud computing paradigm builds on the foundations of distributed computing, grid computing, virtualisation, service orientation, etc. Cloud storage, which is one of the most attractive cloud services, offers numerous benefits from both the technology and functionality perspectives such as increased availability, flexibility, and functionality. Therefore, Cloud-based platforms have become fundamental to collaborative and data management systems. However, the new Cloud environment creates major collaborative and data management challenges such as efficient and reliable remote storage, online data query processing, data integrity, outsourcing computation, and secure virtualisation, among others. Such issues need to be carefully studied and solved to enable the wide deployment and adoption of Cloud computing in businesses, industry, etc.The purpose of this special issue is to publish recent advances in Cloud-based collaborative systems and Cloud storage. The special issue comprises five high-quality papers, which are arranged as follows. and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset.In the third paper, 3 "Sensor Data Management in the Cloud: Data Storage, Data Ingestion, and Data Retrieval," the authors address an increasing need to capture, store, and analyse the dynamic semistructured data from sensors. A similar growth of semistructured data in the modern Web has led to the creation of NoSQL data stores for scalability, availability, and performance, whereas large-scale data processing frameworks for parallel analysis.The authors study how sensor data management can benefit from MongoDB with Apache Spark technologies, specifically for ingesting high-velocity sensor data and parallel retrieval of high volume data. The performance of MongoDB sharding and no-sharding databases with Apache Spark are evaluated to identify the right software environment for sensor data management. Park et al in the fourth paper, 4 "Study on the SDN-IP based Solution of Well-known Bottleneck Problems in Private Sector of National R&E Network for BigData Transfer," deal with issues arising in Software Defined Networking (SDN). The key feature of SDN networks is the provisioning of dynamic service architecture. Therefore, it is more appropriate for the big data science computing trends, for handling today's big data science, which requires massive parallel processing and a constant demand for additional capacity and on demand connectivity. The authors bring the experience from KREONET (Korea Research Environment Open Network) and Korea National R&E Network, which adopt SDN as a platform service for big data scientific application. The big data application for KREONET SDN service comprises high-energy physics, astrophysics, biology/genetics, meteorology, artificial satellite data, etc.The last paper 5 "Improved Outsourced Private Set Intersection Protocol Based on Polynomial Interpolation" by Yang et al investigates issues ...

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Social media marketing through time‐aware collaborative filtering

Cited by 17 publications

References 53 publications

Rating and Comments Mining Using TF-IDF and SO-PMI for Improved Priority Ratings

Rating and Comments Mining Using TF-IDF and SO-PMI for Improved Priority Ratings

Information filtering in evolving online networks

Special issue on advanced techniques for cloud data storage and collaborative systems

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