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.
Citations
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TL;DR: In experiments, the proposed hot-topic recommendation system, which is based on the well-known Latent Dirichlet Allocation (LDA) algorithm, was shown to outperform existing recommendation systems with regard to computational efficiency as well as prediction accuracy.
Abstract: Several types of online chat system have been developed; however, there exist no recommendation systems for the recommendation of topics suitable for discussion with a given individual at a particular time. This paper proposes a hot-topic recommendation system based on analysis of the tweets posted by the user, his/her chat partners, and similar users of his/her chat partners, as well as hashtags trending in Twitter. In experiments, the proposed system, which is based on the well-known Latent Dirichlet Allocation (LDA) algorithm, was shown to outperform existing recommendation systems with regard to computational efficiency as well as prediction accuracy.
1 citations
Additional excerpts
...using memory-based and model-based methods [3, 15, 18, 24, 35]....
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18 Jul 2022
TL;DR: Wang et al. as mentioned in this paper proposed a Ranking-based Federated POI Recommendation with Geographic Effect (RFPG), which allows users to reserve their private data on local devices to secure privacy.
Abstract: Point of Interest (POI) recommendation system rec-ommends places in which users have never been to but may be in-terested. Traditionally, it centrally collects contextual information and interaction data to model users' preferences, which raises many privacy concerns. The current studies habitually sacrifice the recommendation performance to cope with privacy con-cerns. To protect users' privacy while ensuring the performance of the POI recommendation system, we propose a Ranking-based Federated POI Recommendation with Geographic Effect (RFPG). The RFPG allows users to reserve their private data on local devices to secure privacy. It adaptively constructs an active region to model the geographic effect, enhancing users' personalized preference modeling. In addition, we design a probability-based negative sampling method to protect privacy further and improve recommendation performance. This method calculates the probability of a POI being a negative sample through POI geographic distribution, then combines the positive samples to construct a local triplet training dataset. Theoretical analysis and experiments on two real datasets demonstrate that our proposed RFPG improves the performance of the POI recommendation while protecting users' private data.
1 citations
Posted Content•
TL;DR: A spatio-temporal activity-centers algorithm to model users’ behavior more accurately and results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques.
Abstract: With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem. However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobility patterns where we find out that users' check-ins happen around several centers depending on their current temporal state. Next, we propose a spatio-temporal activity-centers algorithm to model users' behavior more accurately. Finally, we demonstrate the effectiveness of our proposed contextual model by incorporating it into the matrix factorization model under two different settings: i) static and ii) temporal. To show the effectiveness of our proposed method, which we refer to as STACP, we conduct experiments on two well-known real-world datasets acquired from Gowalla and Foursquare LBSNs. Experimental results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques. Also, we demonstrate the effectiveness of capturing geographical and temporal information for modeling users' activity centers and the importance of modeling them jointly.
1 citations
Cites background or methods from "Incorporating Spatial, Temporal, an..."
...– LMFT [26]: A method that applies temporal information on the user’s recent activities and multiple visits to a POI....
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...One of the most important challenges that limit the accuracy of POI recommendation is the data sparsity problem [1,26,25]....
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01 Apr 2017
TL;DR: A collaborative filtering algorithm in which the process parameters are optimized referring to the most similar well-controlled historical records in an environment-sensitive process manufacturing is proposed and numerical results show that using the Euclidean similarity can achieve a better performance.
Abstract: Optimizing the process parameters is recognized as one of the most important steps to reduce the manufacturing variance. In this paper, we proposed a collaborative filtering (CF) algorithm in which the process parameters are optimized referring to the most similar well-controlled historical records in an environment-sensitive process manufacturing. A performance metrics was proposed and numerical studies were conducted to demonstrate the algorithm's validity. The numerical results show that using the Euclidean similarity can achieve a better performance. The findings suggest that the CF algorithm can be effectively applied to not only commerce domain but also manufacturing domain and offer another applicable option to accomplish the precise manufacturing.
1 citations
References
More filters
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
9,873 citations
"Incorporating Spatial, Temporal, an..." refers background in this paper
...Given a set of users U and a set of items I , a recommender system attempts to find the subset of items that are the most relevant to each user (U j ) [20]....
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TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: 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.
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.
5,686 citations
27 Sep 1999
TL;DR: Some of the research challenges in understanding context and in developing context-aware applications are discussed, which are increasingly important in the fields of handheld and ubiquitous computing, where the user?s context is changing rapidly.
Abstract: When humans talk with humans, they are able to use implicit situational information, or context, to increase the conversational bandwidth. Unfortunately, this ability to convey ideas does not transfer well to humans interacting with computers. In traditional interactive computing, users have an impoverished mechanism for providing input to computers. By improving the computer’s access to context, we increase the richness of communication in human-computer interaction and make it possible to produce more useful computational services. The use of context is increasingly important in the fields of handheld and ubiquitous computing, where the user?s context is changing rapidly. In this panel, we want to discuss some of the research challenges in understanding context and in developing context-aware applications.
4,842 citations
DOI•
01 Jan 1901
2,946 citations
"Incorporating Spatial, Temporal, an..." refers methods in this paper
...To give credit to a larger set of coratings, we scale (1) by the Jaccard similarity index [23]...
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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