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 context- and preference- aware model (CPAM) to incorporate both contextual influence and user preferences into POI recommendation is proposed and the experimental results show that the proposed model outperforms the state-of-the-art baselines.
Abstract: The effective Point-of-Interest (POI) recommendation can significantly assist users to find their preferred POIs and help POI owners to attract more customers. As a result, a variety of methods have been proposed to tackle the issue of POI recommendation recently. However, it is still very difficult to precisely model the strong correlations between the POIs visited by the user and the POIs to be visited next, which leads to the poor performance of POI recommendation. In this paper, we propose a context- and preference- aware model (CPAM) to incorporate both contextual influence and user preferences into POI recommendation. Firstly, we design a Skip-Gram based POI Embedding Model (SG-PEM) to capture the contextual influence of POIs and learn the vector representation (embedding) of POIs from visiting sequences. The users’ preferences for the target POIs are obtained from the learned embeddings via similarity metric. Secondly, for the implicit feedback information contained in the check-in data, we use the Logistic Matrix Factorization (LMF) algorithm to model the users’ personalized preferences for POI. Finally, we unify SG-PEM and LMF as the CPAM model to perform personalized recommendation by leveraging contextual influence and user preferences. The experimental results on two real-world datasets of Foursquare and Gowalla show that the proposed model outperforms the state-of-the-art baselines.
22 citations
TL;DR: This work proposes a new recommendation model based on the perspective of user sessions, namely GACOforRec, which can handle long-term and stable preferences at the same time and preserve the hierarchy of potential preferences.
Abstract: The biggest challenge to recommendation systems based on user preferences is how to improve the ability of the recommendation system to mine and analyse user preferences and behaviours. In this process, we must not only consider the continuation of the user's long-term preference but also improve the system's ability to accommodate short-term preferences and discrete preferences. To this end, we focus on the performance of time factors of user preferences. However, the issue we are concerned about has not received much attention in the existing research. We propose a new recommendation model based on the perspective of user sessions, namely GACOforRec. This model can handle long-term and stable preferences at the same time and preserve the hierarchy of potential preferences. We conducted a large number of comparative experiments on two real datasets, and the results show that GACOforRec is significantly better than other state-of-the-art methods in the study of user sessions.
20 citations
Cites background from "Incorporating Spatial, Temporal, an..."
...Some researchers have now recognized the importance of considering both temporal and spatial information [3], [8], [22], [26]....
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TL;DR: A new online-to-offline service recommendation method based on a novel customer network and service location (CNLRec) in order to help customer to choose the “ideal” O2O services from a large set of alternatives.
Abstract: We propose a new online-to-offline (O2O) service recommendation method based on a novel customer network and service location (CNLRec) in order to help customer to choose the ideal O2O services fro ...
17 citations
Cites background from "Incorporating Spatial, Temporal, an..."
...providing a list of locations that are likely to be relevant to their needs and interests [54]....
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01 Jan 2019
TL;DR: A spatio-temporal-based CF approach provides a combined model to utilize both a spatial and temporal context in ratings simultaneously, thereby providing effective and accurate predictions.
Abstract: Context-aware recommender systems (CARS) have been extensively studied and effectively implemented over the past few years. Collaborative filtering (CF) has been established as a successful recommendation technique to provide web personalized services and products in an efficient way. In this chapter, we propose a spatio-temporal-based CF method for CARS to incorporate spatio-temporal relevance in the recommendation process. To deal with the new-user cold start problem, we exploit demographic features from the user’s rating profile and incorporate this into the recommendation process. Our spatio-temporal-based CF approach provides a combined model to utilize both a spatial and temporal context in ratings simultaneously, thereby providing effective and accurate predictions. Considering a user’s temporal preferences in visiting various venues to achieve better personalization, a genetic algorithm (GA) is used to learn temporal weights for each individual. Experimental results demonstrate that our proposed schemes using two benchmark real-world datasets outperform other traditional schemes.
15 citations
TL;DR: Experimental results, with respect to rating prediction quality and recommendation performance on both public available and large created contextual datasets, show that the proposal outperforms the existing recommender systems especially on the created datasets.
15 citations
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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