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: A spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth based on the temporal characteristics of user trajectories that is proved to be effective and accurate in the IoT.
Abstract: The temporal and spatial characteristics of users are involved in most Internet of Things (IoT) applications. The spatial and temporal movement patterns of users are the most direct manifestation of the temporal and spatial characteristics. The user’s interests, activities, experience and other characteristics are reflected by mobile mode. In view of the low clustering efficiency of moving objects in convergent pattern mining in the IoT, a spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth is proposed. Based on the temporal characteristics of user trajectories, frequent and asynchronous periodic spatiotemporal movement patterns are mined. Firstly, the location sequence is modeled, and the time information is added to the model. Then, a mining algorithm of asynchronous periodic sequential pattern is adopted. The algorithm is based on multiple minimum supports of pattern growth. According to multiple minimum supports, the sequential pattern of asynchronous period is mined deeply and recursively. Finally, the proposed method is validated and evaluated by Gowalla dataset, in which the user characteristics are truly reflected. It is shown by the experimental results that the average pointwise mutual information (PWI) of the proposed algorithm reaches 0.93. And the algorithm is proved to be effective and accurate.
1 citations
27 Feb 2020
TL;DR: This book constitutes the refereed proceedings of the 15th Information Retrieval Technology Conference, AIRS 2019, held in Hong Kong, China, in November 2019.
Abstract: This book constitutes the refereed proceedings of the 15th Information Retrieval Technology Conference, AIRS 2019, held in Hong Kong, China, in November 2019.The 14 full papers presented together with 3 short papers were carefully reviewed and selected from 27 submissions. The scope of the conference covers applications, systems, technologies and theory aspects of information retrieval in text, audio, image, video and multimedia data.
1 citations
Cites background or methods from "Incorporating Spatial, Temporal, an..."
...Some cQA research [3,16,18] leveraged the subtasks of the SemEval...
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...[18], most of neural network-based models exploited supervised data....
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...The analysis of users’ behavior indicates that geographical information has a higher impact on users’ preference than other contextual information [6,18,22]....
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...Several methods have been applied to helpfulness prediction task including support vector regression [9,26,29], probabilistic matrix factorization [23], linear regression [13], extended tensor factorization models [16], HMM-LDA based model [18] and multi-layer neural networks [11]....
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...– 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....
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Posted Content•
TL;DR: Point-of-interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems as mentioned in this paper, which pose several challenges and research questions to the community as a whole.
Abstract: Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements.
29 Oct 2020
TL;DR: In this article, the authors proposed a model which uses heterogeneous context information in the form of a weighted linear combination, where the weights of this combination should be learned for each user separately instead of using the same set of weights for all users.
Abstract: Location Based Social Networks (LBSNs) enable their user to share their check-ins and post reviews about them. The availability of spatial and textual information in LBSNs offers an opportunity to explore user’s history and preferences to find the locations that the user might be interested in. Point-Of-Interests (POIs) spatial features are one of the most important data available on LBSNs as it has a huge impact on user's choice of new location to visit. Users’ reviews and POIs’ categories are another valuable resources of information in LBSNs which help infer users’ interest and POIs’ features. Recent researches attempt to improve the performance of POI recommendation models by making use of different information sources available in social network. In this paper, we examine the impact of using this information on the accuracy of recommendation task. Our major contribution is proposing the model which use heterogeneous context information in the form of a weighted linear combination. We argue that the weights of this combination should be learned for each user separately instead of using the same set of weights for all users. We provide an algorithm for learning the weights for each user such that recommendation accuracy is improved. In addition, it is enable to incorporate extra information source to our proposed model without requirement of changing the model completely or adding extra complexity to it. Experiments conducted on two large datasets of real world, Yelp and Foursquare, shows the effectiveness of the proposed method.
11 Nov 2022
TL;DR: In this paper , a multi-factor interlinked POI recommendation model called MFIP is proposed to extract user similarity for user-sensitive implicit modeling to enrich user representation, which can dynamically adjust the user preferences of different factors to obtain the comprehensive impact of user personalization.
Abstract: The paper proposes a multi-factor interlinked POI recommendation model called MFIP. Extracting user similarity for user-sensitive implicit modeling to enrich user representation. Using contextual information such as sequential, geographical and social to construct a POI recommendation model with collaborative influence of multi-factor, alleviating data sparsity. A novel multi-factor interlinked strategy(FIS) is proposed that can dynamically adjust the user preferences of different factors to obtain the comprehensive impact of user personalization. In addition, we propose a active area selection algorithm based on segmentation to model the geographical information more effectively. Finally, we conduct a comprehensive performance evaluation for MFIP on two large-scale real world check-in datasets collected from Gowalla and Yelp. Experimental results show that MFIP achieves significantly superior recommendation performance compared to other state-of-the-art POI recommendation models.
References
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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
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...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