Probabilistic Social Sequential Model for Tour Recommendation

Rakesh, Vineeth; Jadhav, Niranjan; Kotov, Alexander; Reddy, Chandan K.

doi:10.1145/3018661.3018711

Cited by 21 publications

(11 citation statements)

References 33 publications

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“…We also evaluate our algorithms on a publicly available dataset of Foursquare check-ins [10,27]. Entries in this dataset include location, category and timestamp.…”

Section: Data Extracted Input: Utility Costs and Time Windowsmentioning

confidence: 99%

Orienteering Algorithms for Generating Travel Itineraries

Friggstad

Gollapudi

Kollias

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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We study the problem of automatically and efficiently generating itineraries for users who are on vacation. We focus on the common case, wherein the trip duration is more than a single day. Previous efficient algorithms based on greedy heuristics suffer from two problems. First, the itineraries are often unbalanced, with excellent days visiting top attractions followed by days of exclusively lower-quality alternatives. Second, the trips often re-visit neighborhoods repeatedly in order to cover increasingly low-tier points of interest. Our primary technical contribution is an algorithm that addresses both these problems by maximizing the quality of the worst day. We give theoretical results showing that this algorithm's competitive factor is within a factor two of the guarantee of the best available algorithm for a single day, across many variations of the problem. We also give detailed empirical evaluations using two distinct datasets: (a) anonymized Google historical visit data and (b) Foursquare public check-in data. We show first that the overall utility of our itineraries is almost identical to that of algorithms specifically designed to maximize total utility, while the utility of the worst day of our itineraries is roughly twice that obtained from other approaches. We then turn to evaluation based on human raters who score our itineraries only slightly below the itineraries created by human travel experts with deep knowledge of the area.

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“…We also evaluate our algorithms on a publicly available dataset of Foursquare check-ins [10,27]. Entries in this dataset include location, category and timestamp.…”

Section: Data Extracted Input: Utility Costs and Time Windowsmentioning

confidence: 99%

Orienteering Algorithms for Generating Travel Itineraries

Friggstad

Gollapudi

Kollias

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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“…They use the Markov model to capture the dependence of a POI li+1 on its preceding POI li in a trip as the transition probability from li to li+1. Rakesh et al [23] also assume that each POI visit depends on its preceding POI. They unify such dependency with other factors (e.g., POI popularities) into a latent topic model.…”

Section: Poi Inference Modelmentioning

confidence: 99%

“…The choice of "Claypots Seafood Bar" can be impacted by not only "Botanic Garden" but also the fact that the user is a seafood lover and that "Claypots Seafood Bar" is highly rated by other users. Most existing models [16,23] learn the impact of each factor separately and simply combine them by linear summation, which may not reflect the joint impact accurately.…”

Section: Introductionmentioning

confidence: 99%

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A Joint Context-Aware Embedding for Trip Recommendations

Ramamohanarao

2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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Trip recommendation is an important location-based service that helps relieve users from the time and efforts for trip planning. It aims to recommend a sequence of places of interest (POIs) for a user to visit that maximizes the user's satisfaction. When adding a POI to a recommended trip, it is essential to understand the context of the recommendation, including the POI popularity, other POIs co-occurring in the trip, and the preferences of the user. These contextual factors are learned separately in existing studies, while in reality, they impact jointly on a user's choice of a POI to visit. In this study, we propose a POI embedding model to jointly learn the impact of these contextual factors. We call the learned POI embedding a contextaware POI embedding. To showcase the effectiveness of this embedding, we apply it to generate trip recommendations given a user and a time budget. We propose two trip recommendation algorithms based on our contextaware POI embedding. The first algorithm finds the exact optimal trip by transforming and solving the trip recommendation problem as an integer linear programming problem. To achieve a high computation efficiency, the second algorithm finds a heuristically optimal trip based on adaptive large neighborhood search. We perform extensive experiments on real datasets. The results show that our proposed algorithms consistently outperform state-of-the-art algorithms in trip recommendation quality, with an advantage of up to 43% in F1-score.

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“…The NextPlace framework proposed in [20] can predict the next check-in location, the arrival time, and duration of the stay. The work in [17] proposed a novel model to predict a user's preference of a sequence of locations for the purpose of tour recommendation. In a very recent work [9], the authors proposed a generic predictive model termed 'TribeFlow' which not only mines and predicts user trajectories but can also be used for next product recommendation.…”

Section: Related Work 51 Human Movement Predictionmentioning

confidence: 99%

Recurrent Spatio-Temporal Point Process for Check-in Time Prediction

Yang

Cai

Reddy

2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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We introduce a new problem, namely, check-in time prediction where the goal is to predict the time when a given user will check-in to a location of interest. We design a novel Recurrent Spatio-Temporal Point Process (RSTPP) model for check-in time prediction. RSTPP addresses two key challenges: 1) Data scarcity due to uneven distribution of check-ins among users/locations. 2) User trajectories contain valuable information that is ignored by standard temporal point process which only considers historical event times. RSTPP is designed to learn the latent dependencies of event times over both historical events and spatio-temporal information about locations a user visited before check-in to the location of interest. We evaluate RSTPP on several real-world datasets, and it significantly outperforms state-of-the-art event time predicting techniques. Our work derives a set of practical implications that can benefit a wide spectrum of applications.

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Probabilistic Social Sequential Model for Tour Recommendation

Cited by 21 publications

References 33 publications

Orienteering Algorithms for Generating Travel Itineraries

Orienteering Algorithms for Generating Travel Itineraries

A Joint Context-Aware Embedding for Trip Recommendations

Recurrent Spatio-Temporal Point Process for Check-in Time Prediction

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