The amount of publicly available geo-referenced data has seen a dramatic explosion over the past few years. Human activity generates data and traces that are often transparently annotated with location and contextual information. At the same time, it has become easier than ever to collect and combine rich and diverse location information. For instance, in the context of geoadvertising, the use of geosocial data for targeted marketing is receiving significant attention from a wide spectrum of companies and organizations. With the advent of smartphones and online social networks, a multi-billion dollar industry that utilizes geosocial data for advertising and marketing has emerged. Geotagged social-media posts, GPS traces, data from cellular antennas and WiFi access points are used on a wide scale to directly access people for advertising, recommendations, marketing, and group purchases. Exploiting this torrent of geo-referenced data provides a tremendous potential to materially improve existing recommendation services and offer novel ones, with clear benefits in many domains, including social networks, marketing, and tourism. It also raises issues in the area of responsibility, accountability, transparency, fairness, adequacy (e.g., avoiding ads in improper places) and preventing misconduct.
Achieving the full potential of geo-referenced data requires new technologies to collect, store, analyze and use the data. It also raises issues in the area of responsibility, accountability, transparency, fairness, adequacy (e.g., avoiding ads in improper places) and preventing misconduct. This in turn means addressing many core challenges and combining ideas and techniques from various research communities, such as recommender systems, data management, geographic information systems, social network analytics and text mining. By bringing together researchers and practitioners from these communities, the LocalRec workshop aims to provide a unique forum for discussing in depth and collecting feedback about challenges, opportunities, novel techniques, and applications related to location-based recommendation, geosocial networks and geoadvertising.
Proceeding Downloads
Spatially and semantically diverse points extraction using hierarchical clustering
Diverse points extraction is an important process in the fields of location-based services and automated driving, among others. While existing research has investigated the selection of semantically diverse locations, the selection of points of interest ...
Spatiotemporal prediction of foot traffic
- Samiul Islam,
- Dhruv Gandhi,
- Justin Elarde,
- Taylor Anderson,
- Amira Roess,
- Timothy F. Leslie,
- Hamdi Kavak,
- Andreas Züfle
Foot traffic is a business term to describe the number of customers that enter a point of interest (POI). This work aims to predict future foot traffic: the number of people from each census block group (CBG) that will visit each POI of a study region ...
Predicting customer poachability from locomotion intelligence
Businesses constantly seek out customers who are open to testing competitor offerings. While prior research mostly studies consumer surveys and within-store transactions to identify such customers, the current paper analyzes Third-Party mobile phone ...
Finding "retro" places in Japan: crowd-sourced urban ambience estimation
Understanding the ambience of an area is essential for making various geographical decisions. This kind of ambience (e.g., "beautiful," "quiet," "happy," "retro," etc.) is due to not only physical features, such as scenery and functionality, but also ...
HYPO: skew-resilient partitioning for trajectory datasets
The rapid increase of GPS-enabled devices has led to immense amounts of trajectory data being collected and analyzed. To provide insight into these datasets, a number of spatio-temporal queries need to be executed efficiently and at scale. One such ...
Representation learning of urban regions via mobility-signature-based zone embedding: a case study of Seoul, South Korea
As urbanization continues to evolve and accelerate, understanding the interactions between urban geography and large-scale mobility data has generated a great interest in the urban studies in recent years. In this paper, we present a method to learn ...
Mining points of interest via address embeddings: an unsupervised approach
Digital maps are commonly used across the globe for exploring places that users are interested in, commonly referred to as points of interest (PoI). In online food delivery platforms, PoIs could represent any major private compounds where customers ...
Which portland is it?: a machine learning approach
This paper reviews several approaches to the problem of toponym resolution for news articles referring to 'Portland.' We train several models to differentiate between Portland, Maine and Portland, Oregon, generating features using only the text of the ...
Visualizing accessibility with choropleth maps
We present a system to visualize accessibility to various destinations from essential institutions such as schools and hospitals to common attractions such as beaches. Our visualization system supports real-time computations of driving distances by ...
Recommendations
LocalRec 2019 workshop report: The Third ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and Geoadvertising: Chicago, Illinois, USA --- November 5, 2019
The amount of publicly available geo-referenced data has seen a dramatic explosion over the past few years. Many user activities generate data that are annotated with location and contextual information. Furthermore, it has become easier to collect and ...
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| LocalRec '19 | 12 | 6 | 50% |
| LocalRec'18 | 4 | 3 | 75% |
| LocalRec'17 | 10 | 8 | 80% |
| Overall | 26 | 17 | 65% |