Spatio-Temporal Topic Modeling in Mobile Social Media for Location Recommendation

Hu, Bo; Jamali, Mohsen; Ester, Martin

doi:10.1109/icdm.2013.139

Cited by 61 publications

(26 citation statements)

References 8 publications

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“…Most of the recent research on recommendation systems focuses on combining different types of information. Works described in [12,[25][26][27][28][29] and [30] use temporal information as well as historical preferences of users to make time-aware recommendations. Besides temporal information, location and social network information are used by many recommendation methods.…”

Section: Related Workmentioning

confidence: 99%

Making recommendations by integrating information from multiple social networks

2016

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It is becoming a common practice to use recommendation systems to serve users of web-based platforms such as social networking platforms, review web-sites, and e-commerce web-sites. Each platform produces recommendations by capturing, maintaining and analyzing data related to its users and their behavior. However, people generally use different web-based platforms for different purposes. Thus, each platform captures its own data which may reflect certain aspects related to its users. Integrating data from multiple platforms may widen the perspective of the analysis and may help in modeling users more effectively. Motivated by this, we developed a recommendation framework which integrates data collected from multiple platforms. For this purpose, we collected and anonymized datasets which contain information from several social networking and social media platforms, namely BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm. The collected and integrated data forms a consolidated repository that may become a valuable source for researchers and practitioners. We implemented a number of recommendation methodologies to observe their performance for various cases which involve using single versus multiple features from a single source versus multiple sources. The conducted

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Section: Related Workmentioning

confidence: 99%

Making recommendations by integrating information from multiple social networks

2016

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“…Different from [32], our proposed GTAG method is a graph-based method that exploits the geographical and temporal influences in an integrated way. Hu et al [12] propose a topic-model based approach named Spatio-Temporal Topic (STT), which exploits the spatio-temporal aspects of user checkins for time-aware POI recommendation. In STT, each user has distributions over topics and regions, and each time slot has distributions over topics and POIs.…”

Section: Poi Recommendationmentioning

confidence: 99%

Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences

Yuan

Cong

Sun

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

209

132

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The availability of user check-in data in large volume from the rapid growing location-based social networks (LBSNs) enables a number of important location-aware services. Point-of-interest (POI) recommendation is one of such services, which is to recommend POIs that users have not visited before. It has been observed that: (i) users tend to visit nearby places, and (ii) users tend to visit different places in different time slots, and in the same time slot, users tend to periodically visit the same places. For example, users usually visit a restaurant during lunch hours, and visit a pub at night. In this paper, we focus on the problem of time-aware POI recommendation, which aims at recommending a list of POIs for a user to visit at a given time. To exploit both geographical and temporal influences in time-aware POI recommendation, we propose the Geographical-Temporal influences Aware Graph (GTAG) to model check-in records, geographical influence and temporal influence. For effective and efficient recommendation based on GTAG, we develop a preference propagation algorithm named Breadth-first Preference Propagation (BPP). The algorithm follows a relaxed breathfirst search strategy, and returns recommendation results within at most 6 propagation steps. Our experimental results on two realworld datasets show that the proposed graph-based approach outperforms state-of-the-art POI recommendation methods substantially.

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“…Both [12] and [22] determine user interests based on the time and categories of POIs visited, with [12] employing a topic model and [22] using matrix factorization for location prediction. Based on 68 features such as unique POI categories visited and most visited POI categories, [2] performs location prediction using Ranking Support Vector Machines [13] and Gradient Boosted Regression Trees [32].…”

Section: Related Workmentioning

confidence: 99%

Recommending Tours and Places-of-Interest based on User Interests from Geo-tagged Photos

Lim

2015

Proceedings of the 2015 ACM SIGMOD on PhD Symposium

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Photo sharing sites like Flickr and Instagram have grown increasingly popular in recent years, resulting in a large amount of uploaded photos. In addition, these photos contain useful meta-data such as the taken time and geo-location. Using such geo-tagged photos and Wikipedia, we propose an approach for recommending tours based on user interests from his/her visit history. We evaluate our proposed approach on a Flickr dataset comprising three cities and find that our approach is able to recommend tours that are more popular and comprise more places/points-of-interest, compared to various baselines. More importantly, we find that our recommended tours reflect the ground truth of real-life tours taken by users, based on measures of recall, precision and F1-score.

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Spatio-Temporal Topic Modeling in Mobile Social Media for Location Recommendation

Cited by 61 publications

References 8 publications

Making recommendations by integrating information from multiple social networks

Making recommendations by integrating information from multiple social networks

Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences

Recommending Tours and Places-of-Interest based on User Interests from Geo-tagged Photos

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