A Platform for Privacy-Preserving Geo-social Recommendation of Points of Interest

Riboni, Daniele; Bettini, Cláudio

doi:10.1109/mdm.2013.53

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…• Comparability. This data set has been utilized in a variety of relevant works, including location recommendation [16,17,64], urban activity pattern understanding [10,11], locationbased services with privacy awareness [46,62], etc. Another two Foursquare data sets from [28] and [76] are also leveraged in relevant pieces of research.…”

Section: Data Setmentioning

confidence: 99%

Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modeling

Yang

Eickhoff

2018

ACM Trans. Inf. Syst.

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Many social network applications depend on robust representations of spatio-temporal data. In this work, we present an embedding model based on feed-forward neural networks which transforms social media check-ins into dense feature vectors encoding geographic, temporal, and functional aspects for modelling places, neighborhoods, and users. We employ the embedding model in a variety of applications including location recommendation, urban functional zone study, and crime prediction. For location recommendation, we propose a Spatio-Temporal Embedding Similarity algorithm (STES) based on the embedding model.In a range of experiments on real life data collected from Foursquare, we demonstrate our model's effectiveness at characterizing places and people and its applicability in aforementioned problem domains. Finally, we select eight major cities around the globe and verify the robustness and generality of our model by porting pre-trained models from one city to another, thereby alleviating the need for costly local training.In this work, we instead represent places by means of embedding vectors in a semantic space. Aiming to annotate places in terms of temporal, geographic, and functional aspects, we extract the time, location, and venue function from check-in records and train our model in the context of check-in sequences which originate from an individual user or neighborhood.In comparison with the traditional discrete method, our approach describes places in a continuous manner and preserves more information about people's real behavior as well as places' day-to-day usage patterns. For instance, in the case of label annotation, three food related places which serve Chinese breakfast, pizza, and sushi, respectively, may all be labeled as Restaurant but their features in food type, active hours, and location may vary dramatically. In the course of this article, we will show how our embedding model represents such within-class variance in a natural way. As embedding vectors are learnt from people's real-time check-ins, we also leverage them in user representation to reflect people's activity patterns and interests.The embedding model is an accurate descriptor of places and users in terms of geographic and functional affinity, activity preferences, and daily schedules. As a consequence, it can be applied in a wide range of settings. In this paper, we consider three practical applications: location recommendation, urban functional zone study, and crime prediction. Our empirical investigation is driven by five research questions: • RQ1. How well does the embedding model differentiate locations and users along temporal, geographic, and functional aspects? • RQ2. How does our location recommendation algorithm STES compare to state-of-the-art methods? • RQ3. How to define and visualize urban functional zones using the proposed model? • RQ4. How well can the model predict typical urban characteristics? • RQ5. With what generalization error can an embedding model trained in one city be transferred to other cities?By answer...

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Section: Data Setmentioning

confidence: 99%

Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modeling

Yang

Eickhoff

2018

ACM Trans. Inf. Syst.

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“…With the increasing number of positioning innovations and the popularity of mobile devices, indoor location-based services, that is, indoor navigation [1], recommendation [2], and mobile advertising [3], are expected to witness a significant growth in the next decade. Currently, positioning method using WiFi received signal strength (RSS) has become a promising solution in indoor space, which is mainly attributed to the widespread deployment of WLAN infrastructure.…”

Section: Introductionmentioning

confidence: 99%

BigLoc: A Two-Stage Positioning Method for Large Indoor Space

Zheng

Chen

et al. 2016

International Journal of Distributed Sensor Networks

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With the rapid development of WLAN infrastructure, fingerprint-based positioning using signal strength has become a promising localization solution in indoor space. Commonly fingerprint-based positioning methods face two challenges in large indoor space, one is floor recognition in large building with multifloor, and the other is signal strength variance due to heterogeneous devices and environmental factors. In this paper, we propose a novel two-stage positioning approach to address these challenges of fingerprintbased positioning methods in large indoor space. Firstly, we design a floor-level recognition feature based on WiFi access points and the RSS values to recognize floor. For solving the signal strength variance problem, we propose a new metric to capture the similarity of location fingerprints probability distribution using KL Divergence. To demonstrate the utility of our approach, we have performed comprehensive experiments in a large indoor building.

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Privacy Protection in Mobile Recommender Systems: A Survey

Yan

2016

Security, Privacy, and Anonymity in Computation, Communication, and Storage

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A Platform for Privacy-Preserving Geo-social Recommendation of Points of Interest

Cited by 5 publications

References 5 publications

Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modeling

Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modeling

BigLoc: A Two-Stage Positioning Method for Large Indoor Space

Privacy Protection in Mobile Recommender Systems: A Survey

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