A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks
Abstract
The popularity of location-based social networks available on mobile devices means that large, rich datasets that contain a mixture of behavioral (users visiting venues), social (links between users), and spatial (distances between venues) information are available for mobile location recommendation systems. However, these datasets greatly differ from those used in other online recommender systems, where users explicitly rate items: it remains unclear as to how they capture user preferences as well as how they can be leveraged for accurate recommendation. This paper seeks to bridge this gap with a three-fold contribution. First, we examine how venue discovery behavior characterizes the large check-in datasets from two different location-based social services, Foursquare and Go Walla: by using large-scale datasets containing both user check-ins and social ties, our analysis reveals that, across 11 cities, between 60% and 80% of users' visits are in venues that were not visited in the previous 30 days. We then show that, by making constraining assumptions about user mobility, state-of-the-art filtering algorithms, including latent space models, do not produce high quality recommendations. Finally, we propose a new model based on personalized random walks over a user-place graph that, by seamlessly combining social network and venue visit frequency data, obtains between 5 and 18% improvement over other models. Our results pave the way to a new approach for place recommendation in location-based social systems.
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Tracking locations and analysing mobility has contributed to many day-to-day applications such as traffic management, routing, recommender systems, and cellular network load balancing.
“Tracking locations and analysing mobility has contributed to many day-to-day applications, for example, traffic management and forecasting [87,88], routing individuals [89] and delivery packages [90], recommender systems [91], dynamic load balancing systems for cellular networks [92], to name a few.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [91]
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- Authors
- Noulas, A., Scellato, S., Lathia, N., & Mascolo, C.
- Affiliations
- University of Cambridge (GB)
- ORCID
- Lathia, Neal
- Venue
- 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing
- Publisher
- IEEE
- Published
- 2012-09
- Pages
- 144-153
- OpenAlex
- W1967635926
- Cited by
- 184
- References
- 23
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 5 · 2021: 9 · 2020: 13 · 2019: 20 · 2018: 22 · 2017: 26 · 2016: 20 · 2015: 29 · 2014: 27 · 2013: 10
- FWCI
- 16.61
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Social Computing (publishedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Keywords
- venue recommendation, location-based social networks, check-in data, latent space models, user mobility, social ties, Foursquare, recommender systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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