Semantic-Based Location Recommendation With Multimodal Venue Semantics

Wang, Xiangyu; Zhao, Yiliang; Nie, Liqiang; Gao, Yue; Nie, Weizhi; Zha, Zheng-Jun; Chua, Tat-Seng

doi:10.1109/tmm.2014.2385473

Cited by 73 publications

(25 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These social services are often connected to each other by deploying the so-called cross-linking functionality [4]. With multi-source and mobility-related data, understanding user behaviours via user profile learning is promising and possible [13] [15][17] [25] [26].…”

Section: Introductionmentioning

confidence: 99%

Harvesting Multiple Sources for User Profile Learning

Farseev

Nie

Akbari

et al. 2015

Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Self Cite

View full text Add to dashboard Cite

User profile learning, such as mobility and demographic profile learning, is of great importance to various applications. Meanwhile, the rapid growth of multiple social platforms makes it possible to perform a comprehensive user profile learning from different views. However, the research efforts on user profile learning from multiple data sources are still relatively sparse, and there is no large-scale dataset released towards user profile learning. In our study, we contribute such benchmark and perform an initial study on user mobility and demographic profile learning. First, we constructed and released a large-scale multi-source multimodal dataset from three geographical areas. We then applied our proposed ensemble model on this dataset to learn user profile. Based on our experimental results, we observed that multiple data sources mutually complement each other and their appropriate fusion boosts the user profiling performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Harvesting Multiple Sources for User Profile Learning

Farseev

Nie

Akbari

et al. 2015

Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Self Cite

View full text Add to dashboard Cite

show abstract

“…They did not fully exploit the preferences at aspect level and also had no provision of recommendation explanation. Wang et al [8] exploited multi-modal (i.e. text, image, etc.)…”

Section: Releated Workmentioning

confidence: 99%

Personalized Aspect based Recommender System in Social Networks

2018

IJRTER

View full text Add to dashboard Cite

Abstract-The social network in current age populates opinion on various products, services, persons through ratings and reviews. Unlike ratings, the reviews can help users to elaborate their opinion and share the extent of consumption experience in various aspects. Though some of the existing recommendation systems have been using the user reviews, the interpretability of the models were erroneous. The aspect based sentiments exploited with qualitative model can lead to the qualitative recommendations. This paper proposed a modeled that exploits the correlation among aspects in review using deep neural network. It formulates the preference of users' on aspect category as a bipartite relation, represents it as a location-aspect category bipartite graph, and models the explainable recommendation with the notion of ordered dense subgraph extraction using bipartite core-based and ranking-based approaches, and it evaluates the generated recommendation with three datasets.

show abstract

“…Location-Based Social Networks (LBSNs) [Bao et al 2015;Wang et al 2015b] have recently become increasingly popular, such as Foursquare. 3 In LBSNs, users check in and share their experiences about POIs with friends.…”

Section: Check-in-based Recommendationmentioning

confidence: 99%

Augmented Collaborative Filtering for Sparseness Reduction in Personalized POI Recommendation

Cui

Shen

Nie

et al. 2017

ACM Trans. Intell. Syst. Technol.

Self Cite

View full text Add to dashboard Cite

As mobile device penetration increases, it has become pervasive for images to be associated with locations in the form of geotags. Geotags bridge the gap between the physical world and the cyberspace, giving rise to new opportunities to extract further insights into user preferences and behaviors. In this article, we aim to exploit geotagged photos from online photo-sharing sites for the purpose of personalized Point-of-Interest (POI) recommendation. Owing to the fact that most users have only very limited travel experiences, data sparseness poses a formidable challenge to personalized POI recommendation. To alleviate data sparseness, we propose to augment current collaborative filtering algorithms along from multiple perspectives. Specifically, hybrid preference cues comprising user-uploaded and user-favored photos are harvested to study users' tastes. Moreover, heterogeneous high-order relationship information is jointly captured from user social networks and POI multimodal contents with hypergraph models. We also build upon the matrix factorization algorithm to integrate the disparate sources of preference and relationship information, and apply our approach to directly optimize user preference rankings. Extensive experiments on a large and publicly accessible dataset well verified the potential of our approach for addressing data sparseness and offering quality recommendations to users, especially for those who have only limited travel experiences.

show abstract

Semantic-Based Location Recommendation With Multimodal Venue Semantics

Cited by 73 publications

References 33 publications

Harvesting Multiple Sources for User Profile Learning

Harvesting Multiple Sources for User Profile Learning

Personalized Aspect based Recommender System in Social Networks

Augmented Collaborative Filtering for Sparseness Reduction in Personalized POI Recommendation

Contact Info

Product

Resources

About