Spatial topic modeling in online social media for location recommendation

Hu, Bo; Ester, Martin

doi:10.1145/2507157.2507174

Cited by 164 publications

(97 citation statements)

References 15 publications

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“…Both data sets were previously used for recommendation evaluation in [10]. The Yelp data set contains 45,981 users, 229,906 ratings of 1-5 scales, 11,537 POIs, plus text reviews on POIs.…”

Section: Methodsmentioning

confidence: 99%

“…Many POI recommendation approaches are based on topic modeling, for example, STM [10] and LCA [29] predict the probability of visiting a POI, for which the error specific metrics such as RMSE/MAE are incomputable because probabilities are not comparable with ratings. For this reason, we evaluate the following methods.…”

Section: Rating Accuracy Of Individual Poismentioning

confidence: 99%

“…Some examples in this category include [4], [13], which consider no content information, [10], [16], and [29], which consider content as side information. The key difference between trip recommendation and POI recommendation is that POI recommendation considers neither the order of visiting POIs nor the time budget of users and the POI availability constraint.…”

Section: Poi Recommendationmentioning

confidence: 99%

See 2 more Smart Citations

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

Zhang

Liang

Wang

et al. 2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP hard problem and previous solutions do not consider two practical and important constraints. One constraint is POI availability where a POI may be only available during a certain time window. Another constraint is uncertain traveling time where the traveling time between two POIs is uncertain. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real life LBSN data sets.

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“…Both data sets were previously used for recommendation evaluation in [10]. The Yelp data set contains 45,981 users, 229,906 ratings of 1-5 scales, 11,537 POIs, plus text reviews on POIs.…”

Section: Methodsmentioning

confidence: 99%

Section: Rating Accuracy Of Individual Poismentioning

confidence: 99%

Section: Poi Recommendationmentioning

confidence: 99%

See 1 more Smart Citation

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

Zhang

Liang

Wang

et al. 2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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“…User annotations in the form of tips and comments are analysed collectively to extract general topics to characterise places or to extract collective sentiment indications about the place. Examples of works that considered place categories are [14][15][16][17]. In [14,15], the latent Dirichlet allocation (LDA) model was used to represent places as a probability distribution over topics collected from tags and categories or comments made in a place and, similarly, aggregate all tips from places a user has visited to model a user's interest.…”

Section: Related Workmentioning

confidence: 99%

“…Aggregation was necessary, as terms associated with a single POI are usually short, incomplete, and ambiguous. [16] on the other hand modelled topics from tweets and reviews from Twitter and Yelp and assumed that the relations between user interests and location are derived from the topic distributions for both users and locations. In [17], a probabilistic approach is proposed that utilises geographic, social, and categorical correlations among users and places to recommend new POIs from historical check-in data of all users.…”

Section: Related Workmentioning

confidence: 99%

Spatio-semantic user profiles in location-based social networks

Mohamed

Abdelmoty

2017

Int J Data Sci Anal

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Knowledge of users' visits to places is one of the keys to understanding their interest in places. Usercontributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users' association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets.

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