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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TL;DR: The results indicate that while modeling geographical and temporal influences can improve recommendation quality, fusing all other contextual information into a recommendation model is not always the best strategy.
Abstract: As the popularity of Location-based Social Networks increases, designing accurate models for Point-of-Interest (POI) recommendation receives more attention. POI recommendation is often performed by incorporating contextual information into previously designed recommendation algorithms. Some of the major contextual information that has been considered in POI recommendation are the location attributes (i.e., exact coordinates of a location, category, and check-in time), the user attributes (i.e., comments, reviews, tips, and check-in made to the locations), and other information, such as the distance of the POI from user’s main activity location and the social tie between users. The right selection of such factors can significantly impact the performance of the POI recommendation. However, previous research does not consider the impact of the combination of these different factors. In this article, we propose different contextual models and analyze the fusion of different major contextual information in POI recommendation. The major contributions of this article are as follows: (i) providing an extensive survey of context-aware location recommendation; (ii) quantifying and analyzing the impact of different contextual information (e.g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, which can incorporate all the major contextual information into a single recommendation model; and (iii) evaluating the considered models using two well-known real-world datasets. Our results indicate that while modeling geographical and temporal influences can improve recommendation quality, fusing all other contextual information into a recommendation model is not always the best strategy.
8 citations
TL;DR: The improved collaborative filtering recommendation algorithm has greatly improved the prediction accuracy and the effectiveness of the MRAPP algorithm is demonstrated.
Abstract: Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP (Media Recommendation Algorithm based on Popularity Prediction). The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated.
7 citations
Cites background from "Incorporating Spatial, Temporal, an..."
...(e basic idea of collaborative filtering is that users have the same or similar values, ideas, knowledge level, and interest preferences, and their demand for information is similar [6, 7]....
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04 May 2022
TL;DR: This work proposes its own collaborative filtering recommendation model, which models users’ activity areas by using a Gaussian distribution model, and then calculates the similarity between users to establish similar user groups and demonstrates that the proposed approach outperforms other state-of-the-art POI recommendation methods.
Abstract: With the development of mobile internet and social platforms, the lifestyle of check-in has become popular in people’s daily life. With the support of highly accurate positioning technology, the social platform has accumulated a large amount of user check-in data. Based on these data, the platform can provide point-of-initerest recommendation services for users.However, existing recommendation algorithms often suffer from some limitations: (1) Recommendation systems using deep learning algorithms require extremely high hardware computing power and can not be deployed on edge devices; (2) General recommendation algorithms do not fully exploit the potential of users to go to new points of interest when making recommendations. Based on the above existing problems, we propose our own collaborative filtering recommendation model: (1) The model contains simple operations and requires very little device computing power; (2) It models users’ activity areas by using a Gaussian distribution model, and then calculates the similarity between users to establish similar user groups. The similarity can be used as the probability of regional transfer of group members. Based on this method, the points-of-interest of similar users are added to the recommendation list for the purpose of exploring new interest points. The experimental results on the well-known dataset Gowalla demonstrate that the proposed approach outperforms other state-of-the-art POI recommendation methods.
5 citations
TL;DR: This paper proposes a social context-aware mobile data offloading algorithm to maximize the quality of service (QoS) of users in a small cell backhaul offloading environment and demonstrates that the proposed algorithm outperforms the other algorithms that do not consider the social context.
Abstract: In recent years, total mobile traffic has increased explosively, and this has led to severe traffic load on the mobile network operator (MNO)'s core network. Mobile data offloading is a promising solution to alleviate the core network's load. Through such mobile data offloading, MNO can reroute the mobile traffic to other access networks using various radio access technologies, such as WiFi, opportunistic communications, and so on. In addition, social networking traffic has risen sharply due to the popularity of online social networking services, such as Facebook and Twitter. Thus, we need to consider a social context for effective mobile data offloading. In this paper, in order to apply the social context to mobile data offloading, we model the social context in terms of two aspects: the user's social relationships and application's popularity. In addition, we propose a social context-aware mobile data offloading algorithm to maximize the quality of service (QoS) of users in a small cell backhaul offloading environment. The performance evaluation results demonstrate that the proposed algorithm outperforms the other algorithms that do not consider the social context.
5 citations
Cites background from "Incorporating Spatial, Temporal, an..."
...In [20], they proposed a framework to improve the quality of recommendations in location-based social networks, which exploits social context as well as spatial and temporal context in a collaborative filtering algorithm....
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27 Aug 2019
TL;DR: The experimental results obtained by using a real-world dataset of tweets show that the proposed method to recommend travel routes to social media users is effective in recommending travel routes achieving remarkable precision and recall rates.
Abstract: On line social networks (e.g., Facebook, Twitter) allow users to tag their posts with geographical coordinates collected through the GPS interface of smart phones. The time- and geo-coordinates associated with a sequence of tweets manifest the spatial-temporal movements of people in real life. The paper presents an approach to recommend travel routes to social media users exploiting historic mobility data, social features of users and geographic characteristics of locations. Travel routes recommendation is formulated as a ranking problem aiming at minimg the top interesting locations and travel sequences among them, and exploit such information to recommend the most suitable travel routes to a target user. A ranking function that exploits users' similarity in visiting locations and in travelling along mobility paths is used to predict places the user could like. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending travel routes achieving remarkable precision and recall rates.
3 citations
Cites background from "Incorporating Spatial, Temporal, an..."
...Specifically, [3], [5], [6], [7], [9] deposit peoples location histories into a user-location matrix where a row corresponds to a users’ location history and each column denotes a venue like a restaurant....
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Abstract: Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.
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01 Jan 1901
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21 Aug 2011
TL;DR: A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance.
Abstract: Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.
2,922 citations