Incorporating Spatial, Temporal, and Social Context in Recommendations for Location-Based Social Networks
01 Dec 2016-IEEE Transactions on Computational Social Systems (Institute of Electrical and Electronics Engineers (IEEE))-Vol. 3, Iss: 4, pp 164-175
TL;DR: A novel approach to incorporate spatial, temporal, and social context into a traditional collaborative filtering algorithm is introduced, and it is demonstrated that this approach is at the least competitive with existing state-of-the-art location recommenders.
Abstract: Location-based social networks (LBSNs) such as Foursquare, Brightkite, and Gowalla are a growing area where recommendation algorithms find a practical application. With an ever-increasing variety of venues to choose from deciding on a destination can be overwhelming. Recommenders aid their users in the decision-making process by providing a list of locations likely to be relevant to the user’s needs and interests. Traditional collaborative filtering algorithms consider relationships between users and locations, finding users to be similar only if their location histories overlap. However, the availability of spatial, temporal, and social information in an LBSN offers an opportunity to improve the quality of a recommendation engine. Social network data allows us to connect users who can directly influence each other’s decisions. Temporal data allows us to account for the drifting preferences of users, giving more weight to recent location visits over historical selections, and taking advantages of repetitive behaviors. Spatial information allows us to focus recommendations on locations close to the user, keeping our recommendations relevant as a user travels. We introduce a novel approach to incorporate spatial, temporal, and social context into a traditional collaborative filtering algorithm. We evaluate our method on data sets collected from three LBSNs, and demonstrate that our approach is at the least competitive with existing state-of-the-art location recommenders.
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Patent•
eBay1
TL;DR: In this paper, the authors described improved capabilities for a computer program product embodied in a computer readable medium that, when executing on one or more computers, helps determine an unknown user's preferences through the use of internet based social interactive graphical representations on a computer facility by performing the steps of ascertaining preferences of a plurality of users who are part of an internet-based social interactive construct.
Abstract: In embodiments of the present invention improved capabilities are described for a computer program product embodied in a computer readable medium that, when executing on one or more computers, helps determine an unknown user's preferences through the use of internet based social interactive graphical representations on a computer facility by performing the steps of (1) ascertaining preferences of a plurality of users who are part of an internet based social interactive construct, wherein the plurality of users become a plurality of known users; (2) determining the internet based social interactive graphical representation for the plurality of known users; and (3) inferring the preferences of an unknown user present in the internet based social interactive graphical representation of the plurality of known users based on the interrelationships between the unknown user and the plurality of known users within the graphical representation.
215 citations
TL;DR: This article strives to present a systematic introduction of DAO, including its concept and characteristics, research framework, typical implementations, challenges, and future trends, including a novel reference model for DAO which employs a five-layer architecture.
Abstract: Decentralized autonomy is a long-standing research topic in information sciences and social sciences. The self-organization phenomenon in natural ecosystems, the Cyber Movement Organizations (CMOs) on the Internet, and the Distributed Artificial Intelligence (DAI), and so on, can all be regarded as its early manifestations. In recent years, the rapid development of blockchain technology has spawned the emergence of the so-called Decentralized Autonomous Organization [DAO, sometimes labeled as Decentralized Autonomous Corporation (DAC)], which is a new organization form that the management and operational rules are typically encoded on blockchain in the form of smart contracts, and can autonomously operate without centralized control or third-party intervention. DAO is expected to overturn the traditional hierarchical management model and significantly reduce organizations’ costs on communication, management, and collaboration. However, DAO still faces many challenges, such as security and privacy issue, unclear legal status, and so on. In this article, we strive to present a systematic introduction of DAO, including its concept and characteristics, research framework, typical implementations, challenges, and future trends. Especially, a novel reference model for DAO which employs a five-layer architecture is proposed. This article is aimed at providing helpful guidance and reference for future research efforts.
136 citations
Cites background from "Incorporating Spatial, Temporal, an..."
...For example, DAO can accomplish automatic matching of roles/tasks, through digitizing information and behavior data of individuals/organizations (such as users’ click, search, and browse data), and match individuals’ positions and roles in a DAO according to their contributions and abilities, then automatically complete task identification, recommendation [36], and matching....
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01 Jan 2018
TL;DR: This chapter delves into the cognitive radio (CR) and its social relations and makes sufficient exploits in establishing a scheme that will be based on social-based cooperative sensing scheme (SBC).
Abstract: The mobile networks seem to have a steady future in the direction of the recent emergence of socially aware cognitive mobile networks. Their style and design are specifically made in improving shared spectrum space access, in cooperative spectrum sensing, and in enhancing device-to-device communications. Socially aware mobile networks do have enough potential to amass sufficient returns in the efficacy of the spectrum and also to march and gain a considerable amount of increase in the capacity of the network. Even though there are lot of gains in its potency to be reaped yet, still there seems to be enough challenges that are both businessand technical-related that have to be taken care of. This chapter delves into the cognitive radio (CR) and its social relations and also makes sufficient exploits in establishing a scheme that will be based on social-based cooperative sensing scheme (SBC).
85 citations
07 Nov 2019
TL;DR: An effective geographical model is proposed by considering the user's main region of activity and the relevance of each location within that region and is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation.
Abstract: With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings. Due to the scarcity of check-in data, the availability of geographical information offers an opportunity to improve the accuracy of POI recommendation. Moreover, matrix factorization methods provide effective models which can be used in POI recommendation. However, there are two main challenges which should be addressed to improve the performance of POI recommendation methods. First, leveraging geographical information to capture both the user’s personal, geographic profile and a location’s geographic popularity. Second, incorporating the geographical model into the matrix factorization approaches. To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users’ and locations’ points of view. To this end, an effective geographical model is proposed by considering the user’s main region of activity and the relevance of each location within that region. Then, the proposed local geographical model is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation. Experimental results on two well-known datasets demonstrate that the proposed approach outperforms other state-of-the-art POI recommendation methods.
54 citations
Cites background or methods from "Incorporating Spatial, Temporal, an..."
...incorporated in the recommendation process [8,16,3]. The analysis of users’ behavior indicates that geographical information has a higher impact on users’ preference than other contextual information [18,22,6]. As a consequence, several POI recommendation methods have been proposed considering the geographical context [8,11,12,21]. However, the past work has considered geographical context only from the us...
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...tions at each time slot. – PFMPD: A method using the Power-law Distribution [19] that model people tend to visit nearby POIs. We integrate this model with the Probabilistic Factor Model (PFM). – LMFT [18]: A method that considers a user’s recent activities as more important than their past activities and multiple visits to a location, as indicates of a stronger preference for that location. – iGLSR7 [...
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TL;DR: A new travel RS employed on the mobile device to generate personalized travel planning comprising of multiple Point of Interests (POIs) is presented and the obtained results are found to be proficient by means of improved diversity and accuracy of generated recommendations.
Abstract: With the massive growth of the internet, a new paradigm of recommender systems (RS's) is introduced in various real time applications. In the research for better RS's, especially in the travel domain, the evolution of location-based social networks have helped RS's to understand the changing interests of users. In this article, the authors present a new travel RS employed on the mobile device to generate personalized travel planning comprising of multiple Point of Interests (POIs). The recommended personalized list of travel locations will be predicted by generating a heat map of already visited POIs and the highly relevant POIs will be selected for recommendation as destinations. To enhance the recommendation quality, this article exploits the temporal features for increased user visits. A personalized travel plan is recommended to the user based on the user selected POIs and the proposed travel RS is experimentally evaluated with the real-time large-scale dataset. The obtained results of the developed RS are found to be proficient by means of improved diversity and accuracy of generated recommendations.
48 citations
References
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Proceedings Article•
20 May 2012TL;DR: A social-historical model is proposed to explore user’s check-in behavior on location-based social networks and shows how social and historical ties can help location prediction.
Abstract: Location-based social networks (LBSNs) have become a popular form of social media in recent years. They provide location related services that allow users to “check-in” at geographical locations and share such experiences with their friends. Millions of “check-in” records in LBSNs contain rich information of social and geographical context and provide a unique opportunity for researchers to study user’s social behavior from a spatial-temporal aspect, which in turn enables a variety of services including place advertisement, traffic forecasting, and disaster relief. In this paper, we propose a social-historical model to explore user’s check-in behavior on LBSNs. Our model integrates the social and historical effects and assesses the role of social correlation in user’s check-in behavior. In particular, our model captures the property of user’s check-in history in forms of power-law distribution and short-term effect, and helps in explaining user’s check-in behavior. The experimental results on a real world LBSN demonstrate that our approach properly models user’s checkins and shows how social and historical ties can help location prediction.
249 citations
18 May 2009
TL;DR: GeoLife2.0 can expand a user’s social network, provide them with a trustworthy resource matching their interests and help them sponsor geo-related activities like cycling with minimal effort.
Abstract: GeoLife2.0 is a GPS-data-driven social networking service where people can share life experiences and connect to each other with their location histories. By mining people’s location history, GeoLife can measure the similarity between users and perform personalized friend recommendation for an individual. Later, we can predict the individual’s interest level in the locations visited by their friends while have not been found by them. The locations with relatively high interesting level can be recommended. Therefore, GeoLife2.0 can expand a user’s social network, provide them with a trustworthy resource matching their interests and help them sponsor geo-related activities like cycling with minimal effort.
226 citations
"Incorporating Spatial, Temporal, an..." refers background in this paper
...Index Terms— Collaborative filtering, context-awareness, geographical influence, location-based social networks (LBSNs), location recommendation, temporal effects....
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29 Oct 2012
TL;DR: This paper proposes a geo-social correlation model to capture social correlations on LBSNs considering social networks and geographical distance, and demonstrates that this approach properly models the social correlations of a user's new check-ins by considering various correlation strengths and correlation measures.
Abstract: Location-based social networks (LBSNs) have attracted an increasing number of users in recent years. The availability of geographical and social information of online LBSNs provides an unprecedented opportunity to study the human movement from their socio-spatial behavior, enabling a variety of location-based services. Previous work on LBSNs reported limited improvements from using the social network information for location prediction; as users can check-in at new places, traditional work on location prediction that relies on mining a user's historical trajectories is not designed for this "cold start" problem of predicting new check-ins. In this paper, we propose to utilize the social network information for solving the "cold start" location prediction problem, with a geo-social correlation model to capture social correlations on LBSNs considering social networks and geographical distance. The experimental results on a real-world LBSN demonstrate that our approach properly models the social correlations of a user's new check-ins by considering various correlation strengths and correlation measures.
222 citations
"Incorporating Spatial, Temporal, an..." refers background in this paper
...Thus, explicit or implicit ratings assigned by a person can take on different interpretations depending on context, and a user’s current context can potentially render much of their past item history largely irrelevant to the situation at hand....
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...A user performing a check-in notifies their online friends of their location and provides an opportunity to interact....
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26 Nov 2012
TL;DR: It is proposed that the recommender system should move beyond the conventional accuracy criteria and take some other criteria into account, such as coverage, diversity, serendipity, scalability, adaptability, risk, novelty and so on.
Abstract: Recommender systems now tend to gain popularity and significance. The proliferation of many recommender systems leads to the difficulty of locating a good recommender system. The algorithms contained in the recommender system determine the efficiency of the recommender systems. The question now is to find the most appropriate algorithms to meet users' needs. So far, the research carried out has focused on improving the accuracy of recommender systems. In this paper, we propose that the recommender system should move beyond the conventional accuracy criteria and take some other criteria into account, such as coverage, diversity, serendipity, scalability, adaptability, risk, novelty and so on. Experimental results with data from VELO indicate that people with different interest degree tend to prefer different algorithms; thus the use of various evaluation criteria to judge the performance of algorithm is meaningful.
190 citations
TL;DR: This article presents a task-focused approach to recommendation that is entirely independent of the type of content involved and leverages robust, high-performance, commercial software.
Abstract: A technique that correlates database items to a task adds content-independent context to a recommender system based solely on user interest ratings. In this article, we present a task-focused approach to recommendation that is entirely independent of the type of content involved. The approach leverages robust, high-performance, commercial software. We have implemented it in a live movie recommendation site and validated it with empirical results from user studies.
145 citations