Exploiting Geo-Spatial Preference for Personalized Expert Recommendation

Lu, Haokai; Caverlee, James

doi:10.1145/2792838.2800189

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…More general abstractions have been used to model preference evolution for recommendation such as point processes [15] and recurrent neural networks [43]. Similarly, modeling user actions along with geographical data has been widely explored with probabilistic models [2,8], matrix factorization [32], and tensor factorization [17]. A variety of methods have built on matrix and tensor factorization for cross domain learning [45,46].…”

Section: Related Workmentioning

confidence: 99%

Latent Cross

Beutel

Covington

Jain

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

256

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The success of recommender systems often depends on their ability to understand and make use of the context of the recommendation request. Significant research has focused on how time, location, interfaces, and a plethora of other contextual features affect recommendations. However, in using deep neural networks for recommender systems, researchers often ignore these contexts or incorporate them as ordinary features in the model. In this paper, we study how to effectively treat contextual data in neural recommender systems. We begin with an empirical analysis of the conventional approach to context as features in feed-forward recommenders and demonstrate that this approach is inefficient in capturing common feature crosses. We apply this insight to design a state-of-the-art RNN recommender system. We first describe our RNN-based recommender system in use at YouTube. Next, we offer "Latent Cross," an easy-to-use technique to incorporate contextual data in the RNN by embedding the context feature first and then performing an element-wise product of the context embedding with model's hidden states. We demonstrate the improvement in performance by using this Latent Cross technique in multiple experimental settings.

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Section: Related Workmentioning

confidence: 99%

Latent Cross

Beutel

Covington

Jain

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

256

View full text Add to dashboard Cite

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“…Geographical footprints have also been widely explored in many location-based applications [12,18,20,30,32,35]. One of the most popular applications is POI recommendation, where geographical influence is combined with user preference for better performance [18,30].…”

Section: Related Workmentioning

confidence: 99%

“…One of the most popular applications is POI recommendation, where geographical influence is combined with user preference for better performance [18,30]. Other works have used geographical influence for rating prediction in Yelp [12], activity recommendation with GPS history [35], expert recommendation [20] and event-based group recommendation [32]. In contrast, we are focused on geo-spatial influence for user known-for profile discovery in social media.…”

Section: Related Workmentioning

confidence: 99%

Discovering What You're Known For

Caverlee

Niu

2016

Proceedings of the 10th ACM Conference on Recommender Systems

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Discovering what people are known for is valuable to many important applications such as recommender systems. Unlike an individual's personal interests, what a user is known for is reflected by the views of others, and is often not easily discerned for a long-tail of the vast majority of users. In this paper, we tackle the problem of discovering what users are known for through a probabilistic model called Bayesian Contextual Poisson Factorization. Moving beyond just modeling user's content, it naturally models and integrates additional contextual factors, concretely, user's geospatial footprints and social influence, to overcome noisy online activities and social relations. Through GPS-tagged social media datasets, we find that the proposed method can improve known-for prediction performance by 17.5% in precision and 20.9% in recall on average, and that it can capture the implicit relationships between a user's known-for profile and her content, geo-spatial and social influence.

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“…Recommeder systems: Location-based social networks have promoted the new paradigm of location-aware recommendations, whereby the recommender system exploits the spatial aspect of ratings when producing recommendations. For instance, finding and recommending local experts in social media has been studied extensively in the recent years (Lu & Caverlee, 2015;Niu, Liu, & Caverlee, 2016;Cheng, Caverlee, Barthwal, & Bachani, 2014;W. Li, Eickhoff, & de Vries, 2016).…”

Section: Introductionmentioning

confidence: 99%

Twitter user geolocation by filtering of highly mentioned users

Ebrahimi

ShafieiBavani

Wong

et al. 2018

Asso for Info Science & Tech

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Geolocated social media data provide a powerful source of information about places and regional human behavior. Because only a small amount of social media data have been geolocation-annotated, inference techniques play a substantial role to increase the volume of annotated data. Conventional research in this area has been based on the text content of posts from a given user or the social network of the user, with some recent crossovers between the text-and network-based approaches. This paper proposes a novel approach to categorize highly-mentioned users (celebrities) into Local and Global types, and consequently use Local celebrities as location indicators. A label propagation algorithm is then used over the refined social network for geolocation inference. Finally, we propose a hybrid approach by merging a text-based method as a back-off strategy into our network-based approach. Empirical experiments over three standard Twitter benchmark data sets demonstrate that our approach outperforms state-of-the-art user geolocation methods.

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Exploiting Geo-Spatial Preference for Personalized Expert Recommendation

Cited by 13 publications

References 20 publications

Latent Cross

Latent Cross

Discovering What You're Known For

Twitter user geolocation by filtering of highly mentioned users

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