Application of choice models in tourism recommender systems

Choice models (CM) are proposed in the field of tourism recommender systems (TRS)with the aim of providing algorithms with both a theoretical understanding of tour-ist's motivations and a certain degree of transparency. The goal of this work is toovercome some of the limitations of current state-of-art algorithms used in TRSs byproviding: (1) accurate preferences, which are learnt from user choices rather thanfrom ratings, and (2) interpretable coefficients, which are achieved by means of theset of estimated parameters of CM. The study was carried out with a gastronomicdata set generated in an ecological experiment in the tourism domain. The perfor-mance of CM has been compared with a set of baseline algorithms (rating-based andensembles) by using two evaluation metrics: precision and DCG. The CM out-performed the baseline algorithms when the size of the choice set was limited. Thefindings suggest that CM may provide an optimal trade-off between theoreticalsoundness, interpretability and performance in the field of TRSThis research was sponsored by EMALCSA/Coruña Smart City under grant CSC-14-13, the Ministry of Science and Innovation of Spain under grant TIN2014-56633-C3-1-R, the Ministry of Economy and Competitiveness of Spain under grant MTM2013-41383P, the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019, ED431G/08), and the European Regional Development Fund (ERDF)S

Enhancing User Personalization in Conversational Recommenders

Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, however, only partially explore the user preference space and make limiting assumptions about how user feedback can be best incorporated, resulting in long dialogues and poor recommendation performance. In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance user personalization in the interactive preference elicitation process by prioritizing attributes that most effectively represent the actual preference space of the user; and (ii) a user representation refiner, to effectively fuse together the user preferences collected from the interactive elicitation process to obtain a more personalized understanding of the user. Through extensive experiments on four frequently used datasets, we find the proposed framework not only outperforms all the state-of-the-art conversational recommenders (in terms of both recommendation performance and conversation efficiency), but also provides a more personalized experience for the user under the proposed multi-groundtruth multi-round conversational recommendation setting.Comment: To Appear On TheWebConf (WWW) 202

How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment

We investigate the impact of collaborative filtering recommender algorithms (e.g., Amazon\u27s “Customers who bought this item also bought”) commonly used in e-commerce on sales diversity. We use data from a randomized field experiment run on a top retailer in North America across 82,290 SKUs and 1,138,238 users. We report four main findings. First, we demonstrate across a wide range of product categories that the use of traditional collaborative filters (or CFs) is associated with a decrease in sales diversity relative to a world without product recommendations. Further, the design of the CF matters. CFs based on purchase data are associated with a greater effect size than those based on product views. Second, the decrease in aggregate sales diversity may not always be accompanied by a corresponding decrease in individual-level consumption diversity. In fact, it is even possible for individual consumption diversity to increase while aggregate sales diversity decreases. Third, co-purchase network analysis shows that recommenders can help individuals explore new products but similar users end up exploring the same kinds of products resulting in the concentration bias at the aggregate level. Fourth and finally, there is a difference between absolute and relative impact on niche items. Specifically, absolute sales and views for niche items in fact increase, but their gains are smaller compared to the gains in views and sales for popular items. Thus, while niche items gain in absolute terms, they lose out in terms of market shares

Ensembles of choice-based models for recommender systems

In this thesis, we focused on three main paradigms: Recommender Systems, Decision Making, and Ensembles. The work is structured as follows. First, the thesis analyzes the potential of choice-based models. The motivation behind this was based on the idea of applying sound decisionmaking paradigms, such as choice and utility theory, in the field of Recommender Systems. Second, this research analyzes the cognitive process underlying choice behavior. On the one hand, neural and gaze activity were recorded experimentally from different subjects performing a choice task in a Web Interface. On the other hand, cognitive were fitted using rational, emotional, and attentional features. Finally, the work explores the hybridization of choice-based models with ensembles. The goal is to take the best of the two worlds: transparency and performance. Two main methods were analyzed to build optimal choice-based ensembles: uninformed and informed. First one, two strategies were evaluated: 1-Learner and N-Learners ensembles. Second one, we relied on three types of prior information: (1) High diversity, (2) Low error prediction (MSE), (3) and Low crowd error

Modèle hybride combinant réseau de neurones convolutifs et modèle basé sur le choix pour la recommandation de sièges

Avec la vente de billets en ligne, les consommateurs souhaitant réserver un ticket pour un concert, une pièce de théâtre ou un film ont désormais la possibilité de choisir leur emplacement. Ce choix influence l’expérience vécue : différents facteurs sont à considérer, et chaque client fait son propre raisonnement (plus ou moins consciemment) pour prendre cette décision. Par exemple, dans un cinéma, certaines personnes vont privilégier les sièges au centre pour avoir la meilleure vision possible de l’écran, tandis que d’autres pourront préférer les sièges latéraux pour être moins dérangés par la présence d’autrui, en particulier si beaucoup de sièges au centre sont déjà réservés. Cet exemple illustre l’hétérogénéité de raisonnement d’un consommateur dans cette situation, et met en valeur deux catégories de facteurs influant sur la prise de décision : la position dans la salle, et la proximité aux autres. La réservation en ligne a ainsi permis de collecter ces choix dans des bases de données, et pour l’industrie culturelle (dans notre exemple le gérant de cinéma), cette information peut être cruciale. D’abord, connaître les sièges les plus attractifs à un instant donné peut permettre de modifier la tarification et ainsi augmenter l’affluence dans les salles et donc les recettes. De plus, si cette connaissance se fait spécifiquement pour chaque utilisateur ayant déjà effectué des réservations par le passé, cela peut également permettre d’améliorer les stratégies marketing par la mise en place d’un système de recommandation personnalisé de sièges. Un premier objectif du mémoire est la revue de méthodes permettant l’estimation de l’attractivité d’un siège dans une salle partiellement remplie. Deux stratégies sont possibles : la première consiste à traiter chaque client individuellement afin d’assurer une modélisation personnelle de la prise de décision, mais qui est limité par la quantité de données disponible par clients. L’autre stratégie consiste à regrouper l’ensemble des données pour pouvoir appliquer des modèles avec plus de capacité comme de l’apprentissage profond, mais qui perd l’information du comportement individuel. Une hypothèse de ce mémoire est que malgré une performance plus faible pour la deuxième stratégie, cette dernière apporte de l’information utile, et une combinaison des deux permet d’améliorer la performance globale et de pallier au problème de la stratégie individualisée du possible manque de données.----------ABSTRACT: With online ticket sales, consumers wishing to book a ticket for a concert or a movie now have the opportunity to choose their location. This choice influences the lived experience: different factors have to be considered, and each client makes his own reasoning (more or less consciously) to make this decision. For example, in a movie theatre, some people may prefer centre seats to get the best possible view of the screen, while others may prefer side seats to be less disturbed by the presence of others, especially if many centre seats are already reserved. This example illustrates the heterogeneity of reasoning of a consumer in this situation, and highlights two categories of factors influencing decision making: position in the room, and proximity to others. Online booking has thus made it possible to collect these choices in databases, and for the cultural industry, this information can be crucial. Firstly, knowing the most attractive seats at a given time can help to modify the pricing and thus increase attendance in halls and thus revenues. Moreover, if this knowledge is done specifically for each user who has made reservations in the past, it can also help improve marketing strategies by implementing a personalized seat recommendation system. A first objective here is the review of methods for estimating the attractiveness of a seat in a partially-filled room. Two strategies are possible: the first one is to treat each client individually to ensure personal modeling of decision making, but this is limited by the amount of data available per client. The other strategy is to aggregate the data to be able to apply models with more capacity such as deep learning, but lose the information about individual behaviour. One hypothesis of this paper is that despite weaker performance for the second strategy, the latter provides useful information, and a combination of the two can improve overall performance and overcome the problem of the individualized strategy of the possible lack of data

Endogeneity in adaptive choice contexts: Choice-based recommender systems and adaptive stated preferences surveys

Endogeneity arises in discrete choice models due to several factors and results in inconsistent estimates of the model parameters. In adaptive choice contexts such as choice-based recommender systems and adaptive stated preferences (ASP) surveys, endogeneity is expected because the attributes presented to an individual in a specific menu (or choice situation) depend on the previous choices of the same individual (as well as the alternative attributes in the previous menus). Nevertheless, the literature is indecisive on whether the parameter estimates in such cases are consistent or not. In this paper, we discuss cases where the estimates are consistent and those where they are not. We provide a theoretical explanation for this discrepancy and discuss the implications on the design of these systems and on model estimation. We conclude that endogeneity is not a concern when the likelihood function properly accounts for the data generation process. This can be achieved when the system is initialized exogenously and all the data are used in the estimation. In line with previous literature, Monte Carlo results suggest that, even when exogenous initialization is missing, empirical bias decreases with the number of choices per individual. We conclude by discussing the practical implications and extensions of this research.United States Department of Energy (DOE) Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT PIA/BASAL AFB180003 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 119110

Endogeneity in adaptive choice contexts: Choice-based recommender systems and adaptive stated preferences surveys

Endogeneity arises in discrete choice models due to several factors and results in inconsistent estimates of the model parameters. In adaptive choice contexts such as choice-based recommender systems and adaptive stated preferences (ASP) surveys, endogeneity is expected because the attributes presented to an individual in a specific menu (or choice situation) depend on the previous choices of the same individual (as well as the alternative attributes in the previous menus). Nevertheless, the literature is indecisive on whether the parameter estimates in such cases are consistent or not. In this paper, we discuss cases where the estimates are consistent and those where they are not. We provide a theoretical explanation for this discrepancy and discuss the implications on the design of these systems and on model estimation. We conclude that endogeneity is not a concern when the likelihood function properly accounts for the data generation process. This can be achieved when the system is initialized exogenously and all the data are used in the estimation. In line with previous literature, Monte Carlo results suggest that, even when exogenous initialization is missing, empirical bias decreases with the number of choices per individual. We conclude by discussing the practical implications and extensions of this research.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and Logistic

On the usefulness of mixed logit models with unobserved inter- and intra-individual heterogeneity

Mixed logit models with unobserved inter- and intra-individual heterogeneity hierarchi-cally extend standard mixed logit models by allowing tastes to vary randomly both acrossindividuals as well as across choice tasks encountered by the same individual. Recentwork advocates the use of these methods in choice-based recommender systems underthe premise that mixed logit models with unobserved inter- and intra-individual hetero-geneity afford personalised preference estimation and prediction. In this research note, weevaluate the ability of mixed logit with unobserved inter- and intra-individual heterogene-ity to produce accurate individual-level predictions of choice behaviour. Using simulatedand real data, we show that mixed logit with unobserved inter- and intra-individual het-erogeneity does not provide significant improvements in choice prediction accuracy overstandard mixed logit models, which only account for inter-individual taste variation. Wemake these observations even in scenarios with high levels of intra-individual taste vari-ation and when the number of choice situations per decision-maker is large. Also, theestimation of mixed logit with unobserved inter- and intra-individual heterogeneity re-quires at least ten times as much computation time as the estimation of standard mixedlogit models. Informed by recent advances in machine learning and econometrics, wethen discuss alternative modelling approaches, which can capture richer dependenciesbetween decision-makers, alternatives and attributes