Joint Modeling of User Check-in Behaviors for Point-of-Interest Recommendation

Yin, Hongzhi; Zhou, Xiaofang; Shao, Yingxia; Wang, Hao; Sadiq, Shazia

doi:10.1145/2806416.2806500

Cited by 95 publications

(57 citation statements)

References 38 publications

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“…The challenges regarding location recommendation have already been tackled through different traditional methods [72,76] [78,38,85,83]. CF methods have two categories; memory-based and model-based [8] and study a user's interest regarding each proposed POI.…”

Section: Location Recommendation Using Lbsn Datamentioning

confidence: 99%

“…Moreover, a growing line of research has recently been dedicated to employing various kinds of effects to enhance recommender systems. Geographical [78,85,49], social [7,23], context-oriented [82,83] (e.g. text contents and word-of-mouth) and temporal influences are the commonly utilized factors [83].…”

Section: Location Recommendation Using Lbsn Datamentioning

confidence: 99%

“…Geographical [78,85,49], social [7,23], context-oriented [82,83] (e.g. text contents and word-of-mouth) and temporal influences are the commonly utilized factors [83]. However, despite several benefits of the temporal influence [92], we need to further explore various attributes and dimensions of the temporal influence.…”

Section: Location Recommendation Using Lbsn Datamentioning

confidence: 99%

“…Those using time in a continuous manner claim that choosing a proper time interval is not feasible [83]. However, discrete-time constitutes the basis of our daily lives.…”

Section: Multi-aspect Temporal Influence (Mati) (Chapter 4)mentioning

confidence: 99%

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Location Inference and Recommendation in Social Networks

Hosseini¹

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With the ubiquity of GPS-enabled smartphones, Location-Based Service (LBS) as a prominent product of social networks has become an essential part of our daily lives. People can easily socialize and share their check-in data (location, time, text, photo and etc.) via Location-Based Social Networks (LBSN). Through mining the check-in dataset, Point-Of-Interest(POI) recommendation systems assist users in exploring new attractive venues.The primary challenge regarding POI recommendation is to suggest a list of new interesting locations to the query user. Specifically, the excessive sparsity observed in user-location matrices is the main problem. The well known Collaborative Filtering (CF) methods are commonly used with other factors like geographical, social, context-oriented (e.g. text contents) and temporal effects to promote the effectiveness of the location recommendation systems. Despite the vital role of temporal influence, an insufficient amount of research has been devoted to considering the time factor in location recommendation. While we have an insufficient number of records regarding a user's check-in at a particular location, predicting the time of the visit seems more problematic. We have dedicated part of our research to study various aspects of the temporal influence in the recommendation.Additionally, we use Twitter textual contents to extract a set of spatial phrases associated with each region. Such an act enriches the textual contents of the local POIs. This process enhances location recommendation systems, as it facilitates textual similarity among POI tags and the tweet history of the query user.In short, we aim to address the problems involved with both aspects of Location Inference and Recommendation in social networks. Our research in this thesis has four parts. Firstly, we define a problem which merely considers a single temporal aspect to enhance the performance of a location recommendation task. Subsequently, we develop a probabilistic model which detects a user's temporal orientation based on visibility weights of POIs visited by her during weekday/weekend cycles. While this method is limited to a single temporal scale, the idea can be adapted to other time-related periodic cycles (e.g. daily home-work return trips). Secondly, we argue that the majority of existing methods merely concentrate on a limited number of temporal scales and neglect others. We propose a probabilistic generative model, named after Multi-aspect Time-related Influence (MATI) which employs the user's check-in log to detect her temporal mobility pattern under various scales (e.g. minute, hour, day and so on). It then performs recommendation using multi-aspect temporal correlations between the query user and proposed locations. Thirdly, we further study the role of the time factor in recommendation models. We define a new problem to jointly model a pair of heterogeneous time-related 2 effects (recency and the subset feature) in location recommendation. To address the challenges, we propose a generative model ...

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Section: Location Recommendation Using Lbsn Datamentioning

confidence: 99%

Section: Location Recommendation Using Lbsn Datamentioning

confidence: 99%

Section: Location Recommendation Using Lbsn Datamentioning

confidence: 99%

“…Those using time in a continuous manner claim that choosing a proper time interval is not feasible [83]. However, discrete-time constitutes the basis of our daily lives.…”

Section: Multi-aspect Temporal Influence (Mati) (Chapter 4)mentioning

confidence: 99%

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Location Inference and Recommendation in Social Networks

Hosseini¹

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“…JIM [82] is a joint probabilistic generative model which integrates the temporal effect, geographical-social influence, content effect and word-of-mouth effect. The word-of-mouth effect refers to that the probability of a user visiting a spatial item is largely affected by the popularity of this spatial item at the target region.…”

Section: Comparative Approachesmentioning

confidence: 99%

Point of interests recommendation in location-based social networks

Wang¹

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With the rapid development of location-based social networks (LBSNs), point of interests (POI) recommendation has become an important means to help people discover attractive and interesting locations from billions of locations globally. However, this recommendation is very challenging compared to the traditional recommender systems. A user may visit only a limited number of POIs, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place as most of the items visited by a user are usually located within a short distance from the user's home. Moreover, user interests and behavior patterns may vary dramatically across different time and different geographical regions. On the other hand, in reality, human movement exhibits sequential patterns. Thus, how to predict users' next move based on her previous visited locations is important and challenging in LBSNs. Our project focuses on offering a more accurate and efficient recommender system by overcoming the aforementioned challenges, and it contains the following three parts:In the first part, we design ST-SAGE, a spatial-temporal sparse additive generative model for POI recommendation. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns of POIs and the content of POIs. To further alleviate the data sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called spatial pyramid. To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework.The second part aims to leveraging sequential patterns in POI recommendation. However, this is very challenging, considering 1) users' check-in data in LBSNs has a low sampling rate in both space and time, which renders existing location prediction techniques on GPS trajectories ineffective;2) the prediction space is extremely large, with millions of distinct locations as the next prediction target, which impedes the application of classical Markov chain models; and 3) there is no existing framework that unifies users' personal interests and the sequential influence of recently visited locations in a principled manner. In light of the above challenges, we propose a sequential personalized POI recommendation framework (SPORE) which introduces a novel latent variable topic-region to model and fuse sequential influence with personal interests in the latent and exponential space. The advantages of modeling the sequential effect at the topic-region level include a significantly reduced prediction space, an effective alleviation of data sparsity and a direct expression of the semantic meaning of users' spatial activities. Helen Huang. I also want to thank the PhD students I worked with. I really appreciate having had the opportunity to work with them, and their friendships will always be reme...

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Location‐based social network recommendations with computational intelligence‐based similarity computation and user check‐in behavior

Elangovan

Subramaniyaswamy

Varadarajan

et al. 2020

Concurrency and Computation

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Location recommending frameworks plays a very significant role in suggesting the users with new places to visit especially when users are visiting unfamiliar areas. Most of the existing recommender systems do not consider the fact that different users have different behavior while checking in. Some approaches do not consider the essential factors while providing recommendations. These systems lack adaptability and hence they provide poor recommendations. An adaptive approach to provide users with a personalized recommendation has been proposed in this paper. We have considered three features namely, user activeness feature, user similarity feature, and the spatial feature. In addition to this, we have also considered the location popularity for a given timeslot. We have divided the users into inactive and active based on the degree of activeness on social networks using fuzzy c‐means clustering. We have provided two strategies based on the activeness of the user. A two‐dimensional Gaussian kernel density estimation strategy is used for the active user. A one‐dimensional power‐law function strategy is used for inactive users. Moreover, we have integrated the time‐based popularity of the location and probability estimation based on the similarity between the users. To evaluate the proposed model, we have used a large‐scale Foursquare dataset.

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Joint Modeling of User Check-in Behaviors for Point-of-Interest Recommendation

Cited by 95 publications

References 38 publications

Location Inference and Recommendation in Social Networks

Location Inference and Recommendation in Social Networks

Point of interests recommendation in location-based social networks

Location‐based social network recommendations with computational intelligence‐based similarity computation and user check‐in behavior

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