Wolfgang Wörndl has given an invited talk at the 6th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising (LocalRec) on November 1, 2022. This paper provides a summary of the topics addressed.
{"title":"Utilizing location-based social media for trip mining and recommendation","authors":"W. Wörndl","doi":"10.1145/3557992.3567412","DOIUrl":"https://doi.org/10.1145/3557992.3567412","url":null,"abstract":"Wolfgang Wörndl has given an invited talk at the 6th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising (LocalRec) on November 1, 2022. This paper provides a summary of the topics addressed.","PeriodicalId":184189,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124737006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mikael Brunila, Michael McConnell, Stalgia Grigg, Michael Appuhn, Bethany Sumner, Mitchell Bohman
Most online communication today is inherently temporal and aspatial. Instant messaging (IM) services are structured around a timeline interface which prioritizes a linear succession of events and guides our attention towards the novel. In this way, the different textures of social life are lost in linear reduction. In this paper, we present DRIFT, a novel and open-source IM application framework, based on a different paradigm of communication that preserves temporality but organizes it around space. Instead of the timeline, our application grounds messaging in the map and its pins, offering users a tool that encourages spatio-temporal communication and the sharing of spatial features. Given increasing concerns about the safety and privacy of online user interaction, we integrate state-of-the art encryption as a core feature of our application. Firstly, to protect user messages and map pins, we implement end-to-end encryption with the Double Ratchet key management algorithm and the open standard Matrix protocol. Secondly, to maintain location privacy, we allow users to batch download map tilesets and machine learning models to perform operations such as search entirely on device, avoiding compromising API calls to cloud services. With these combined features, DRIFT aims to introduce a new model for online interaction that upends the short attention span imposed by the narrow timeline and replace it with a spatio-temporally rich and secure IM tool for both laymen and more vulnerable users such as journalists, human rights activists, and whistleblowers.
{"title":"DRIFT","authors":"Mikael Brunila, Michael McConnell, Stalgia Grigg, Michael Appuhn, Bethany Sumner, Mitchell Bohman","doi":"10.1145/3557992.3565987","DOIUrl":"https://doi.org/10.1145/3557992.3565987","url":null,"abstract":"Most online communication today is inherently temporal and aspatial. Instant messaging (IM) services are structured around a timeline interface which prioritizes a linear succession of events and guides our attention towards the novel. In this way, the different textures of social life are lost in linear reduction. In this paper, we present DRIFT, a novel and open-source IM application framework, based on a different paradigm of communication that preserves temporality but organizes it around space. Instead of the timeline, our application grounds messaging in the map and its pins, offering users a tool that encourages spatio-temporal communication and the sharing of spatial features. Given increasing concerns about the safety and privacy of online user interaction, we integrate state-of-the art encryption as a core feature of our application. Firstly, to protect user messages and map pins, we implement end-to-end encryption with the Double Ratchet key management algorithm and the open standard Matrix protocol. Secondly, to maintain location privacy, we allow users to batch download map tilesets and machine learning models to perform operations such as search entirely on device, avoiding compromising API calls to cloud services. With these combined features, DRIFT aims to introduce a new model for online interaction that upends the short attention span imposed by the narrow timeline and replace it with a spatio-temporally rich and secure IM tool for both laymen and more vulnerable users such as journalists, human rights activists, and whistleblowers.","PeriodicalId":184189,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising","volume":"402 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124354736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Often, global and regional topics on Twitter across multiple thematic areas, such as disasters, politics, protests, entertainment, epidemics, literature, travel, culture, weather, etc., witness an unprecedented level of exchange of conversations. An issue with those conversations is that a user can be at location A and participate in a public discourse specific to location B, which we refer to as the Location A/B problem. Location profiling of users solely based on locations mentioned in their tweets leads to ineffective location-based recommendations. The problem is deemed solved if location candidates could be categorized as either origin locations (Location As) or non-origin locations (Location Bs); however, real-world tweets are much more complex, and currently, no public datasets are available for training such classifiers. To the best of our knowledge, this study yields the first steps in addressing the Location A/B problem on Twitter. We propose a theoretical framework that utilizes the existing literature on location inference to categorize location candidates as either origin locations or non-origin locations. We envision that: (i) the framework provides the grounds for designing models that aim to solve the Location A/B problem, and (ii) the location profiling of users based on origin locations leads to improved geotargeted recommendations.
{"title":"Addressing the location A/B problem on Twitter: the next generation location inference research","authors":"Rabindra Lamsal, A. Harwood, M. Read","doi":"10.1145/3557992.3565989","DOIUrl":"https://doi.org/10.1145/3557992.3565989","url":null,"abstract":"Often, global and regional topics on Twitter across multiple thematic areas, such as disasters, politics, protests, entertainment, epidemics, literature, travel, culture, weather, etc., witness an unprecedented level of exchange of conversations. An issue with those conversations is that a user can be at location A and participate in a public discourse specific to location B, which we refer to as the Location A/B problem. Location profiling of users solely based on locations mentioned in their tweets leads to ineffective location-based recommendations. The problem is deemed solved if location candidates could be categorized as either origin locations (Location As) or non-origin locations (Location Bs); however, real-world tweets are much more complex, and currently, no public datasets are available for training such classifiers. To the best of our knowledge, this study yields the first steps in addressing the Location A/B problem on Twitter. We propose a theoretical framework that utilizes the existing literature on location inference to categorize location candidates as either origin locations or non-origin locations. We envision that: (i) the framework provides the grounds for designing models that aim to solve the Location A/B problem, and (ii) the location profiling of users based on origin locations leads to improved geotargeted recommendations.","PeriodicalId":184189,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127678073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Twitter is a popular social networking service where people send short messages called tweets. Tweets contain metadata such as language, hashtags, geotags, and time of creation. We focus on the geotags of tweets. A Geo-tag is georeferenced information that indicates the geographical origin of a tweet. Geotagged tweets provide an excellent opportunity to understand the underlying user behavior. We propose a preference-aware route recommendation method relying on over one billion geotagged tweets. The method can recommend routes based on user preference by extracting a subset of one billion geotagged tweets according to user preference and using that subset to generate a cost function for route discovery. The proposed method assumes that areas with a high density of geotagged tweets are areas of high interest. In other words, if the density of geotagged tweets with user preference is superimposed on the cost of the route search, the users' preference can be considered when recommending a route. We highlight a nighttime route recommendation mechanism for a case study of our method. We hypothesize that geotagged tweets sent out at night indicate human activity at night. In other words, areas with a high density of geo-tagged tweets are considered to be areas that are vibrant at night. In addition, it is empirically clear that nighttime vibrant is also based on brightness. Therefore, we utilize nighttime tweets and nighttime light data to recommend routes. We extract a subset by calculating nighttime from tweet metadata. Tweets data are divided into grids and used to calculate a vibrant grid from a weighted tweets grid and a nighttime lights grid. Edge is weighted from vibrant cell values and road network edge lengths to recommend a vibrant route based on weighted road network edges. We experimented in Shinjuku, Tokyo, Japan, between two stations. As a result, based on the objective evaluation, we recommended a vibrant route.
{"title":"Preference aware route recommendation using one billion geotagged tweets","authors":"Osei Yamashita, Shohei Yokoyama","doi":"10.1145/3557992.3565990","DOIUrl":"https://doi.org/10.1145/3557992.3565990","url":null,"abstract":"Twitter is a popular social networking service where people send short messages called tweets. Tweets contain metadata such as language, hashtags, geotags, and time of creation. We focus on the geotags of tweets. A Geo-tag is georeferenced information that indicates the geographical origin of a tweet. Geotagged tweets provide an excellent opportunity to understand the underlying user behavior. We propose a preference-aware route recommendation method relying on over one billion geotagged tweets. The method can recommend routes based on user preference by extracting a subset of one billion geotagged tweets according to user preference and using that subset to generate a cost function for route discovery. The proposed method assumes that areas with a high density of geotagged tweets are areas of high interest. In other words, if the density of geotagged tweets with user preference is superimposed on the cost of the route search, the users' preference can be considered when recommending a route. We highlight a nighttime route recommendation mechanism for a case study of our method. We hypothesize that geotagged tweets sent out at night indicate human activity at night. In other words, areas with a high density of geo-tagged tweets are considered to be areas that are vibrant at night. In addition, it is empirically clear that nighttime vibrant is also based on brightness. Therefore, we utilize nighttime tweets and nighttime light data to recommend routes. We extract a subset by calculating nighttime from tweet metadata. Tweets data are divided into grids and used to calculate a vibrant grid from a weighted tweets grid and a nighttime lights grid. Edge is weighted from vibrant cell values and road network edge lengths to recommend a vibrant route based on weighted road network edges. We experimented in Shinjuku, Tokyo, Japan, between two stations. As a result, based on the objective evaluation, we recommended a vibrant route.","PeriodicalId":184189,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising","volume":"601 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123177907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Location privacy has long been studied, in order to protect users' location data from untrusted servers. While existing research analyzed location privacy methods with generic utility measures, the lack of application-oriented perspectives imposes challenges for adopting location privacy. This study fills the gap by putting application utility front and center, studying the impacts of location privacy in two concrete case studies. We conduct empirical evaluations with real-world datasets from two large cities, and provide in-depth analysis on the obtained results. Furthermore, we examine the relationship between generic utility and application utility as well as the trade-off between privacy and utility in specific application settings. Our results point out interesting behaviors of the studied privacy methods and can help applications with location privacy decision-making.
{"title":"Co-location and air pollution exposure: case studies on the usefulness of location privacy","authors":"Liyue Fan, Julius Marinak, Ashley Bang","doi":"10.1145/3557992.3565991","DOIUrl":"https://doi.org/10.1145/3557992.3565991","url":null,"abstract":"Location privacy has long been studied, in order to protect users' location data from untrusted servers. While existing research analyzed location privacy methods with generic utility measures, the lack of application-oriented perspectives imposes challenges for adopting location privacy. This study fills the gap by putting application utility front and center, studying the impacts of location privacy in two concrete case studies. We conduct empirical evaluations with real-world datasets from two large cities, and provide in-depth analysis on the obtained results. Furthermore, we examine the relationship between generic utility and application utility as well as the trade-off between privacy and utility in specific application settings. Our results point out interesting behaviors of the studied privacy methods and can help applications with location privacy decision-making.","PeriodicalId":184189,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125879275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geocoding a spatial description is challenging since vernacular place names and vague spatial expressions give uncertainty and ambiguity to the description. Usually, digital gazetteers are used to match geospatial objects to their boundaries. However, gazetteers do not contain all places. Therefore, a number of studies have proposed to enrich gazetteers by estimating and representing the vernacular places. Nevertheless, only a few approaches have taken into account vague spatial expressions such as "nearby", and have represented geospatial objects as sharp boundaries. In this work, we present an automatic workflow to retrieve a location approximation of vague spatial description. We propose a model to estimate a fuzzy representation of each mentioned geospatial information and spatial expressions. Then, we perform information fusion to find a location approximation of a property. Lastly, we demonstrate our proposed method by applying it to the case of French Real Estate advertisements with two real-world datasets in Nice and Paris. Real Estate advertisements allow us to deal with uncertain geospatial objects since avague and exaggerated property location's description is usually provided. Our results show that our proposed method is promising and able to correctly approximate a location from uncertain spatial descriptions.
{"title":"Fuzzy representation of vague spatial descriptions in real estate advertisements","authors":"L. Cadorel, Denis Overal, A. Tettamanzi","doi":"10.1145/3557992.3565994","DOIUrl":"https://doi.org/10.1145/3557992.3565994","url":null,"abstract":"Geocoding a spatial description is challenging since vernacular place names and vague spatial expressions give uncertainty and ambiguity to the description. Usually, digital gazetteers are used to match geospatial objects to their boundaries. However, gazetteers do not contain all places. Therefore, a number of studies have proposed to enrich gazetteers by estimating and representing the vernacular places. Nevertheless, only a few approaches have taken into account vague spatial expressions such as \"nearby\", and have represented geospatial objects as sharp boundaries. In this work, we present an automatic workflow to retrieve a location approximation of vague spatial description. We propose a model to estimate a fuzzy representation of each mentioned geospatial information and spatial expressions. Then, we perform information fusion to find a location approximation of a property. Lastly, we demonstrate our proposed method by applying it to the case of French Real Estate advertisements with two real-world datasets in Nice and Paris. Real Estate advertisements allow us to deal with uncertain geospatial objects since avague and exaggerated property location's description is usually provided. Our results show that our proposed method is promising and able to correctly approximate a location from uncertain spatial descriptions.","PeriodicalId":184189,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121794153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Choosing a store (i.e. grocery, restaurant etc.) depends on different decision criteria. If the data for these criteria is distributed among different sources a user might need to invest a substantial amount of time to aggregate the necessary information from different resources or base their decisions only on a subset of criteria. Additionally, visualising all criteria can augment the user's decision making. In this work, we demonstrate a prototype that is able to combine the data of different decision criteria from different (online) resources and provides recommendations of the combined decision criteria. Additionally, a skyline facilitates the choice of stores that dominate specific features. As a concrete example, we state a query of the type "Get me all stores of a supermarket (of a particular company) in the vicinity". The data for the chosen criteria of traffic time, distance, or occupancy of stores were obtained from Google traffic, popular times and timeline. The timeline data is used for our introduced decision criterion 'utility' which is an indicator of how much added value is gained by visiting a particular store. The visualization of this allows users to see at one glance different criteria of their decision-making of which supermarket to choose, which can make the difference between an efficient and hassle-free groceries experience and one that comes at a high cost of money, time and nerves.
{"title":"Doing groceries again: towards a recommender system for grocery stores selection","authors":"Daniyal Kazempour, M. Oelker, Peer Kröger","doi":"10.1145/3557992.3565993","DOIUrl":"https://doi.org/10.1145/3557992.3565993","url":null,"abstract":"Choosing a store (i.e. grocery, restaurant etc.) depends on different decision criteria. If the data for these criteria is distributed among different sources a user might need to invest a substantial amount of time to aggregate the necessary information from different resources or base their decisions only on a subset of criteria. Additionally, visualising all criteria can augment the user's decision making. In this work, we demonstrate a prototype that is able to combine the data of different decision criteria from different (online) resources and provides recommendations of the combined decision criteria. Additionally, a skyline facilitates the choice of stores that dominate specific features. As a concrete example, we state a query of the type \"Get me all stores of a supermarket (of a particular company) in the vicinity\". The data for the chosen criteria of traffic time, distance, or occupancy of stores were obtained from Google traffic, popular times and timeline. The timeline data is used for our introduced decision criterion 'utility' which is an indicator of how much added value is gained by visiting a particular store. The visualization of this allows users to see at one glance different criteria of their decision-making of which supermarket to choose, which can make the difference between an efficient and hassle-free groceries experience and one that comes at a high cost of money, time and nerves.","PeriodicalId":184189,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131962158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising","authors":"","doi":"10.1145/3557992","DOIUrl":"https://doi.org/10.1145/3557992","url":null,"abstract":"","PeriodicalId":184189,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128254255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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