Estimating a latent-class user model for travel recommender systems

Arentze, Theo; Kemperman, Astrid; Aksenov, Petr

doi:10.1007/s40558-018-0105-z

Cited by 10 publications

(3 citation statements)

References 39 publications

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“…Such systems can be useful for trip planning as the recommendation describes a collection of venues for a trip or a route. Some POI group recommendation systems were the subject of recent papers (Cenamor et al 2017;Rakesh et al 2017;Arentze et al 2018;Wörndl et al 2017). Trip planning has an indirect connection to POI lists because a trip, as an abstract set of POIs, can be thought as a POI list with the characteristics posed in this paper: a collection of related POIs.…”

Section: Poi Sequence Recommendationsmentioning

confidence: 99%

Point-of-interest lists and their potential in recommendation systems

Stamatelatos

Drosatos

Gyftopoulos

et al. 2021

Inf Technol Tourism

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Location based social networks, such as Foursquare and Yelp, have inspired the development of novel recommendation systems due to the massive volume and multiple types of data that their users generate on a daily basis. More recently, research studies have been focusing on utilizing structural data from these networks that relate the various entities, typically users and locations. In this work, we investigate the information contained in unique structural data of social networks, namely the lists or collections of items, and assess their potential in recommendation systems. Our hypothesis is that the information encoded in the lists can be utilized to estimate the similarities amongst POIs and, hence, these similarities can drive a personalized recommendation system or enhance the performance of an existing one. This is based on the fact that POI lists are user generated content and can be considered as collections of related POIs. Our method attempts to extract these relations and express the notion of similarity using graph theoretic, set theoretic and statistical measures. Our approach is applied on a Foursquare dataset of two popular destinations in northern Greece and is evaluated both via an offline experiment and against the opinions of local populace that we obtain via a user study. The results confirm the existence of rich similarity information within the lists and the effectiveness of our approach as a recommendation system.

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Section: Poi Sequence Recommendationsmentioning

confidence: 99%

Point-of-interest lists and their potential in recommendation systems

Stamatelatos

Drosatos

Gyftopoulos

et al. 2021

Inf Technol Tourism

View full text Add to dashboard Cite

show abstract

“…The planning attributes were focused on timing and the constraints of planning decisions and explored whether user decisions regarding transportation mode are mainly driven by routine, while the choice of start time of activities is more individual and impulsive. In addition, Artenze et al [26] developed a latent-class user model for tourists, where they used activity location-based parameters and trip-based parameters (i.e., tourist attraction values, time-use characteristics and point of interest (POI) attributes). With a multi-attribute utility function, personalized optimal tours were offered for the users.…”

Section: Literature Reviewmentioning

confidence: 99%

Trip Chaining Model with Classification and Optimization Parameters

Esztergár-Kiss

2020

Sustainability

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In order to model the complex requirements of users travelling in an urban environment, the relevant parameters for creating activity chains have to be identified. In this study, travel related parameters were collected and grouped into two main types: classification parameters and optimization parameters. In the case of optimization parameters, further grouping was performed where general and comfort parameters were introduced. Additionally, the possible values and data sources of the parameters were identified. A utility function was created to take into account the optimization parameters and the weights. Weights related to comfort optimization parameters were aggregated to decrease the number of required settings by the users. Finally, the features of the proposed optimization algorithm are described. With the identified parameters, aggregated weights and elaborated utility function activity chains can be optimized for users with different requirements.

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“…The emphasis on user-specific latent traits has driven the algorithmic development of recommendation systems over the last decade Almahairi et al [2015], Knijnenburg et al [2012], Isinkaye et al [2015], Karumur et al [2018], Arentze et al [2018], Wang et al [2021]. As these latent variables are highly complex in structure for very large datasets, providing a rigorous analysis of the latent vectors based on data relating users, products, and features can be expected to illuminate salient structures.…”

Section: Introductionmentioning

confidence: 99%

A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System

Truong¹,

Uhlmann²

2022

Preprint

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In this paper we provide a latent-variable formulation and solution to the recommender system (RS) problem in terms of a fundamental property that any reasonable solution should be expected to satisfy. Specifically, we examine a novel tensor completion method to efficiently and accurately learn parameters of a model for the unobservable personal preferences that underly user ratings. By regularizing the tensor decomposition with a single latent invariant, we achieve three properties for a reliable recommender system: (1) uniqueness of the tensor completion result with minimal assumptions, (2) unit consistency that is independent of arbitrary preferences of users, and (3) a consensus ordering guarantee that provides consistent ranking between observed and unobserved rating scores. Our algorithm leads to a simple and elegant recommendation framework that has linear computational complexity and with no hyperparameter tuning. We provide empirical results demonstrating that the approach significantly outperforms current state-ofthe-art methods.Preprint. Under review.

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Estimating a latent-class user model for travel recommender systems

Cited by 10 publications

References 39 publications

Point-of-interest lists and their potential in recommendation systems

Point-of-interest lists and their potential in recommendation systems

Trip Chaining Model with Classification and Optimization Parameters

A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System

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