Followee recommendation in Twitter using fuzzy link prediction

Rodríguez, Fernando Martín; Torres, L.; Garza, Sara E.

doi:10.1111/exsy.12153

Cited by 12 publications

(11 citation statements)

References 39 publications

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“…Moreover, Chen () addressed the recommendation process by using the similarity measurement of interval‐valued FNs. Furthermore, Rodríguez, Torres, and Garza () proposed an approach that tackles contact recommendation in Twitter by means of fuzzy logic. Sánchez‐Moreno, González, Vicente, Batista, and García () applied the collaborative filtering approach to music recommendations for both rating predation and item recommendation.…”

Section: Literature Reviewmentioning

confidence: 99%

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

Yan

Liu

et al. 2019

Expert Systems

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Patients always want a precise diagnosis and appropriate treatment advice when they are diagnosed with a disease. Furthermore, the original information about certain features and symptoms appears in different forms, and the effect of time is always ignored in the process of diagnosis. To overcome these defects, this paper develops a systematic multiperiod hybrid decision support model, which combines the similarity measurement and three‐way decision theory to provide prediction and treatment advice for patients under the fuzzy environment. This multiperiod hybrid decision support model, which considers the effect of time, including transformation module, multiperiod integration module, similarity module, prediction module, and three‐way decision module, provides disease prediction and advice on treatment based on similarities and three‐way decision theory. To validate this model, we construct an illustration composed of four cases, and this ultimately shows that MPH‐SDM can effectively support patients' disease diagnoses and treatment.

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Section: Literature Reviewmentioning

confidence: 99%

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

Yan

Liu

et al. 2019

Expert Systems

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“…Rowe et al present an approach that exploits semantics to gage the topical affinity between a user and potential followees using concept graphs. Rodríguez et al propose a fuzzy logic approach that tackles followee recommendation in Twitter. The approach regards recommendation as a link prediction problem and uses three types of similarity between a pair of users.…”

Section: Related Workmentioning

confidence: 99%

“…Evaluation metrics. For each target user, we evaluate the ranking results of various algorithms using two metrics: (1)precision at rank k ( P@ k ), the proportion of the relevant results in top‐ k ranked results, and (2) success at rank k ( S@ k ), the probability of finding at least one correct followee in the top‐ k ranked results. To get more precise result, in each experiment, we take the average of the 10 target users' performance as the final result.…”

Section: Experimental Evaluationmentioning

confidence: 99%

FRFB: Top‐k Followee Recommendation by exploring the Following Behaviors in social networks

Xue

et al. 2018

Concurrency and Computation

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Summary As social networks such as micro‐blogging sites rapidly grow, deciding whom to follow (followee recommendation) becomes a significantly important problem. Most existing works exclusively rely on two traditional factors: the proximity between two users in the network topology or the similarity of the user‐generated contents in the social network, disregarding the effect of users' following behaviors. The challenge of how to effectively combine these two factors remains largely open. Moreover, most research studies simply sort the scores to find top‐k users, which is time‐consuming, especially for large‐scale networks. In this paper, we propose the idea that “predict users' following behaviors by following behaviors themselves.” We consider a user's following to others as a normal process of dynamic and coherent behavior, and we model the potential propagation of the users' following behaviors. Furthermore, based on our previous research on top‐k selection problem, we propose an effective top‐k followee recommendation algorithm, called FRFB. FRFB has low complexity and high scalability and, moreover, good adaptability to real‐life dynamic social networks. We conduct extensive experiments, with two real social network data sets (Wiki and Twitter), which show that FRFB outperforms the well‐known topology‐based followee recommendation algorithms.

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“…in (Liu et al 2016) authors provide followee recommendations by calculating user relevance scores, using neural networks to combine network topology and content of tweets. In (Rodríguez et al 2016) authors recommend followees, using a fuzzy system that exploits followee similarity along with text similarities. Finally, in (Sharma et al 2016) authors present GraphJet, a recentlydeployed system for real-time content recommendations in Twitter, which is based on a real-time bipartite interaction graph between users and tweets.…”

Section: Collaborative Filteringmentioning

confidence: 99%