From Interest to Location: Neighbor-Based Friend Recommendation in Social Media

Zhu, Jinqi; Lu, Li; Ma, Chunmei

doi:10.1007/s11390-015-1593-3

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…He et al (2017) integrated link and content information and developed the MapReduce distributed computing framework to implement the recommendation of friends in large-scale online community network. Zhu, Lu, and Ma (2015) mined user interests from short messages and proposed the neighbor-based friend recommendation to recommend users with similar interests.…”

Section: Link Predictionmentioning

confidence: 99%

Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm

Song

et al. 2020

Expert Systems with Applications

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Circle structure of online brand communities allows companies to conduct cross-marketing activities by the influence of friends in different circles and build strong and lasting relationships with customers. However, existing works on the friend recommendation in social network do not consider establishing friendships between users in different circles, which has the problems of network sparsity, neither do they study the adaptive generation of appropriate link prediction algorithms for different circle features. In order to fill the gaps in previous works, the intelligent attention allocation link prediction algorithm is proposed to adaptively build attention allocation index (AAI) according to the sparseness of the network and predict the possible friendships between users in different circles. The AAI reflects the amount of attention allocated to the user pair by their common friend in the triadic closure structure, which is decided by the friend count of the common friend. Specifically, for the purpose of overcoming the problem of network sparsity, the AAIs of both the direct common friends and indirect ones are developed. Next, the decision tree (DT) method is constructed to adaptively select the suitable AAIs for the circle structure based on the density of common friends and the dispersion level of common friends' attention. In addition, for the sake of further improving the accuracy of the selected AAI, its complementary AAIs are identified with support vector machine model according to their similarity in value, direction, and ranking. Finally, the mutually complementary indices are combined into a composite one to comprehensively portray the attention distribution of common friends of users in different circles and predict their possible friendships for cross-marketing activities. Experimental results on Twitter and Google + show that the model has highly reliable prediction performance.

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Section: Link Predictionmentioning

confidence: 99%

Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm

Song

et al. 2020

Expert Systems with Applications

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“…While the relation between location and interest interaction among social media users is inconspicuous, there are many works try to solve these problems [86], [191]. Zhu et al [191] presented the neighbor-based friend recommendation system to improve these problems. They mined user interest from short tweets and used the hypercube method to explore multiple topics.…”

Section: ) Location-based Friend Recommendationsmentioning

confidence: 99%

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Yochum

Chang

et al. 2020

IEEE Access

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Linked open data is a relatively new topic area with great potential in a wide range of fields. In the tourism domain, many studies are using linked open data to address the problem of location-based recommendation by integrating data with other linked open datasets to enrich data and tourism content for reacting to the needs of tourists. This work aims not only to present a systematic review and mapping of the linked open data in location-based recommendation system on tourism domain, but also to provide an overview of the current research status in the area. First, we classify journal papers in this area from 2001 to 2018 by the year of publication. Second, we analyze and categorize journal papers by the different recommendation applications including problem formulations, data collections, proposed algorithms/systems, and experimental results. Third, we group the linked open data sources used in location-based recommendation system on tourism. Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data. Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.

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“…However, methods that only consider a single factor are not effective. In terms of breadth, it has become a trend in friend recommendation to consider several attributes such as geographical locations, tags, and interests instead of only one attribute [32], [33]. Furthermore, it is equally important to reveal more user-related information about each attribute, i.e., and in-depth analysis is necessary.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Neural Networks for Friend Recommendations in Edge Computing Environment

et al. 2020

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From Interest to Location: Neighbor-Based Friend Recommendation in Social Media

Cited by 6 publications

References 25 publications

Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm

Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Hybrid Deep Neural Networks for Friend Recommendations in Edge Computing Environment

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