Semantic Enabled Recommender System for Micro-Blog Users

Faralli, Stefano; Tommaso, Giorgia Di; Velardi, Paola

doi:10.1109/icdmw.2016.0144

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…Another study that applied itemset mining to micro-blogs was [44]. Specifically, the authors addressed the problem named WTF (who to follow): the idea is to recommend to users, other users to follow, on the basis of the topical users (popular users such as singers or politicians) and the semantic categories topical users belong to.…”

Section: Related Work On Analysis Of Twitter Messages For Studying Momentioning

confidence: 99%

From Data to Rhizomes: Applying a Geographical Concept to Understand the Mobility of Tourists from Geo-Located Tweets

2020

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In geography, the concept of “rhizome” provides a theoretical tool to conceive the way people move in space in terms of “mobility networks”: the space lived by people is delimited and characterized on the basis of both the places they visited and the sequences of their transfers from place to place. Researchers are now wondering whether in the new era of data-driven geography it is possible to give a concrete shape to the concept of rhizome, by analyzing big data describing movement of people traced through social media. This paper is a first attempt to give a concrete shape to the concept of rhizome, by interpreting it as a problem of “itemset mining”, which is a well-known data mining technique. This technique was originally developed for market-basket analysis. We studied how the application of this technique, if supported by adequate visualization strategies, can provide geographers with a concrete shape for rhizomes, suitable for further studies. To validate the ideas, we chose the case study of tourists visiting a city: the rhizome can be conceived as the set of places visited by many tourists, and the common transfers made by tourists in the area of the city. Itemsets extracted from a real-life data set were used to study the effectiveness of both a topographic representation and a topological representation to visualize rhizomes. In this paper, we study how three different interpretations are actually able to give a concrete and visual shape to the concept of rhizome. The results that we present and discuss in this paper open further investigations on the problem.

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Section: Related Work On Analysis Of Twitter Messages For Studying Momentioning

confidence: 99%

From Data to Rhizomes: Applying a Geographical Concept to Understand the Mobility of Tourists from Geo-Located Tweets

2020

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“…According to the based type of model, collaborative filtering algorithm can be divided into 2 directions. The first one is memory based and the second is model based . Based on these ideas, Arain et al construct intelligent travel information platform based on location base services to predict user travel behavior from user‐generated GPS traces …”

Section: Related Workmentioning

confidence: 99%

“…The first one is memory based and the second is model based. 5,6,23 Based on these ideas, Arain et al construct intelligent travel information platform based on location base services to predict user travel behavior from user-generated GPS traces. [28][29][30]…”

Section: Authority Recommendation and Collaborative Filtering Recommentioning

confidence: 99%

“…Apriori algorithm is a classical algorithm for mining association rules for Boolean items that the core of the algorithm is to find frequent item sets by increasing the number of candidate item set. After the method was put forward, the researchers did a lot of supplement, some of these works made a hit in some areas . In spite of this, there is an improved need for this method, especially in computational efficiency and time.…”

Section: Introductionmentioning

confidence: 99%

“…After the method was put forward, the researchers did a lot of supplement, some of these works made a hit in some areas. 1,2,5,6,17,[21][22][23][24][25] In spite of this, there is an improved need for this method, especially in computational efficiency and time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An improved Apriori–based algorithm for friends recommendation in microblog

Liu

Wang

et al. 2017

Int J Communication

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Summary With the rapid development of Internet, the world has come into a new era of network interaction. Internet development in China makes various kinds of software available to users, by which relatives, close friends, and partners can achieve real‐time interactions. It is acknowledged that the recommended issues and persons are the most important parts for user experience. This study concentrates on how to provide a recommendation for users with a common interest in Microblog. We first illustrate some recommended algorithms based on data mining, and the implementation process of friend‐recommended system is then stated. After that, by integrating the characteristics of the user information, the forwarding and comments on information to associated users, we present an improved Apriori–based algorithm to rank the recommended information with confidence interval. Simulation results demonstrate the superiority and efficiency of our method.

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