TY - GEN
T1 - GhostLink
T2 - 2019 World Wide Web Conference, WWW 2019
AU - Mukherjee, Subhabrata
AU - Günnemann, Stephan
N1 - Publisher Copyright:
© 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.
PY - 2019/5/13
Y1 - 2019/5/13
N2 - Social influence plays a vital role in shaping a user's behavior in online communities dealing with items of fine taste like movies, food, and beer. For online recommendation, this implies that users' preferences and ratings are influenced due to other individuals. Given only time-stamped reviews of users, can we find out who-influences-whom, and characteristics of the underlying influence network? Can we use this network to improve recommendation? While prior works in social-aware recommendation have leveraged social interaction by considering the observed social network of users, many communities like Amazon, Beeradvocate, and Ratebeer do not have explicit user-user links. Therefore, we propose GhostLink, an unsupervised probabilistic graphical model, to automatically learn the latent influence network underlying a review community - given only the temporal traces (timestamps) of users' posts and their content. Based on extensive experiments with four real-world datasets with 13 million reviews, we show that GhostLink improves item recommendation by around 23% over state-of-the-art methods that do not consider this influence. As additional use-cases, we show that GhostLink can be used to differentiate between users' latent preferences and influenced ones, as well as to detect influential users based on the learned influence graph.
AB - Social influence plays a vital role in shaping a user's behavior in online communities dealing with items of fine taste like movies, food, and beer. For online recommendation, this implies that users' preferences and ratings are influenced due to other individuals. Given only time-stamped reviews of users, can we find out who-influences-whom, and characteristics of the underlying influence network? Can we use this network to improve recommendation? While prior works in social-aware recommendation have leveraged social interaction by considering the observed social network of users, many communities like Amazon, Beeradvocate, and Ratebeer do not have explicit user-user links. Therefore, we propose GhostLink, an unsupervised probabilistic graphical model, to automatically learn the latent influence network underlying a review community - given only the temporal traces (timestamps) of users' posts and their content. Based on extensive experiments with four real-world datasets with 13 million reviews, we show that GhostLink improves item recommendation by around 23% over state-of-the-art methods that do not consider this influence. As additional use-cases, we show that GhostLink can be used to differentiate between users' latent preferences and influenced ones, as well as to detect influential users based on the learned influence graph.
KW - Content Analysis
KW - Generative Model
KW - Review Community
KW - Social Influence
KW - Social Recommendation
UR - http://www.scopus.com/inward/record.url?scp=85066888269&partnerID=8YFLogxK
U2 - 10.1145/3308558.3313449
DO - 10.1145/3308558.3313449
M3 - Conference contribution
AN - SCOPUS:85066888269
T3 - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
SP - 1310
EP - 1320
BT - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
PB - Association for Computing Machinery, Inc
Y2 - 13 May 2019 through 17 May 2019
ER -