Predicting trading interactions in an online marketplace through location-based and online social networks

Link prediction is a prominent research direction e.g., for inferring upcoming interactions to be used in recommender systems. Although this problem of predicting links between users has been extensively studied in the past, research investigating this issue simultaneously in multiplex networks is r...

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Bibliographic Details
Published in:Information retrieval (Boston) 2019-04, Vol.22 (1-2), p.55-92
Main Authors: Eberhard, Lukas, Trattner, Christoph, Atzmueller, Martin
Format: Article
Language:English
Subjects:
Computer Science
Constellations
Data Mining and Knowledge Discovery
Data Structures and Information Theory
Information Storage and Retrieval
Location based services
Natural Language Processing (NLP)
Network analysis
Pattern Recognition
Predictions
Recommender systems
Social Media for Personalization and Search
Social networks
Supervised learning
Unsupervised learning
Virtual reality
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Summary:Link prediction is a prominent research direction e.g., for inferring upcoming interactions to be used in recommender systems. Although this problem of predicting links between users has been extensively studied in the past, research investigating this issue simultaneously in multiplex networks is rather rare so far. This is the focus of this paper. We investigate the extent to which trading interactions between sellers and buyers within an online marketplace platform can be predicted based on three different but overlapping networks—an online social network, a location-based social network and a trading network. In particular, we conducted the study in the context of the virtual world Second Life. For that, we crawled according data of the online social network, user information of the location-based social network obtained by specialized bots, and we extracted purchases of the trading network. Overall, we generated and used 57 topological and homophilic features in different constellations to predict trading interactions between user pairs. We focused on both unsupervised as well as supervised learning methods. For supervised learning, we achieved accuracy values up to 92.5 % , for unsupervised learning we obtained nDCG values up to over 97 % and MAP values up to 75 % .
ISSN:1386-4564
1573-7659
DOI:10.1007/s10791-018-9336-z