Mining social ties beyond homophily

Liang, Hongwei; Wang, Ke; Zhu, Feida

doi:10.1109/icde.2016.7498259

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…This prefix based depth-first search (PDFS) tree is somewhat inspired by the subsetfirst depth-first (SFDF) tree in Liang et al [2016], which is used to partition attributes to generate group relationships. In addition to the difference of applications, one major difference is that the PDFS tree is proposed to deal with trips consisting of POIs in different orders; however, the SFDF tree deals with a partition of attributes and the order does not matter.…”

Section: Theorem 4 (1) Every Non-dominated Feasible Route Stored At mentioning

confidence: 99%

Trip Recommendation Meets Real-World Constraints

Zhang

Liang

Wang

2016

ACM Trans. Inf. Syst.

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As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP-hard problem and previous solutions do not consider three practical and important constraints. One constraint is POI availability, where a POI may be only available during a certain time window. Another constraint is uncertain traveling time, where the traveling time between two POIs is uncertain. In addition, the diversity of the POIs included in the trip plays an important role in user's final adoptions. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints and leveraging them to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real-life LBSN datasets.

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Section: Theorem 4 (1) Every Non-dominated Feasible Route Stored At mentioning

confidence: 99%

Trip Recommendation Meets Real-World Constraints

Zhang

Liang

Wang

2016

ACM Trans. Inf. Syst.

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NFRR: A Novel Family Relationship Recognition Algorithm Based on Telecom Social Network Spectrum

Niu

Jiao

Cheng

et al. 2019

IEICE Trans. Inf. & Syst.

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There are different types of social ties among people, and recognizing specialized types of relationship, such as family or friend, has important significance. It can be applied to personal credit, criminal investigation, anti-terrorism and many other business scenarios. So far, some machine learning algorithms have been used to establish social relationship inferencing models, such as Decision Tree, Support Vector Machine, Naive Bayesian and so on. Although these algorithms discover family members in some context, they still suffer from low accuracy, parameter sensitive, and weak robustness. In this work, we develop a Novel Family Relationship Recognition (NFRR) algorithm on telecom dataset for identifying one's family members from its contact list. In telecom dataset, all attributes are divided into three series, temporal, spatial and behavioral. First, we discover the most probable places of residence and workplace by statistical models, then we aggregate data and select the top-ranked contacts as the user's intimate contacts. Next, we establish Relational Spectrum Matrix (RSM) of each user and its intimate contacts to form communication feature. Then we search the user's nearest neighbors in labelled training set and generate its Specialized Family Spectrum (SFS). Finally, we decide family relationship by comparing the similarity between RSM of intimate contacts and the SFS. We conduct complete experiments to exhibit effectiveness of the proposed algorithm, and experimental results also show that it has a lower complexity.

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Mining social ties beyond homophily

Cited by 2 publications

References 26 publications

Trip Recommendation Meets Real-World Constraints

Trip Recommendation Meets Real-World Constraints

NFRR: A Novel Family Relationship Recognition Algorithm Based on Telecom Social Network Spectrum

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