Improving Recommendation Effectiveness: Adapting a Dialogue Strategy in Online Travel Planning

Mahmood, Tariq; Ricci, Francesco; Venturini, Adriano

doi:10.3727/109830510x12670455864203

Cited by 40 publications

(17 citation statements)

References 16 publications

(4 reference statements)

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“…An MDP model, deployed to an online bookstore, was found to generate recommendations which outperformed commercially available collaborative filtering methods in successfully recommending items to users [Shani et al 2005]. In another study, it was found that users of a vacation planning website were more inclined to follow recommendations generated by an MDP than a linear policy, resulting in a more efficient search session for the user [Mahmood et al 2009]. MDP methods are, however, not without drawbacks.…”

Section: Markov Decision Processesmentioning

confidence: 99%

An English-Language Argumentation Interface for Explanation Generation with Markov Decision Processes in the Domain of Academic Advising

Dodson

Mattei

Guerin

et al. 2013

ACM Trans. Interact. Intell. Syst.

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A Markov Decision Process (MDP) policy presents, for each state, an action, which preferably maximizes the expected utility accrual over time. In this article, we present a novel explanation system for MDP policies. The system interactively generates conversational English-language explanations of the actions suggested by an optimal policy, and does so in real time. We rely on natural language explanations in order to build trust between the user and the explanation system, leveraging existing research in psychology in order to generate salient explanations. Our explanation system is designed for portability between domains and uses a combination of domain-specific and domain-independent techniques. The system automatically extracts implicit knowledge from an MDP model and accompanying policy. This MDP-based explanation system can be ported between applications without additional effort by knowledge engineers or model builders. Our system separates domain-specific data from the explanation logic, allowing for a robust system capable of incremental upgrades. Domain-specific explanations are generated through case-based explanation techniques specific to the domain and a knowledge base of concept mappings used to generate English-language explanations.

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Section: Markov Decision Processesmentioning

confidence: 99%

An English-Language Argumentation Interface for Explanation Generation with Markov Decision Processes in the Domain of Academic Advising

Dodson

Mattei

Guerin

et al. 2013

ACM Trans. Interact. Intell. Syst.

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“…In the travel and tourism domain, such systems are for example developed to help the customer in the pre-trip information search and decision making process. Examples of recent research include knowledge-based and conversational approaches to filter destinations and select travel packages (Jannach et al 2009(Jannach et al , 2007Zanker et al 2008), context-aware recommendation of places of interest (Baltrunas et al 2011), mobile recommenders (Ricci 2011), or the development of more intelligent user interaction strategies (Mahmood et al 2009). …”

Section: Relation To Other Workmentioning

confidence: 99%

Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations

Jannach

Zanker

Fuchs

2014

Inf Technol Tourism

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Travel websites and online booking platforms represent today's major sources for customers when gathering information before a trip. In particular, community-provided customer reviews and ratings of various tourism services represent a valuable source of information for trip planning. With respect to customer ratings, many modern travel and tourism platforms-in contrast to several other e-commerce domains-allow customers to rate objects along multiple dimensions and thus to provide more fine-granular post-trip feedback on the booked accommodation or travel package. In this paper, we first show how this multicriteria rating information can help to obtain a better understanding of factors driving customer satisfaction for different segments. For this purpose, we performed a Penalty-Reward contrast analysis on a data set from a major tourism platform, which reveals that customer segments significantly differ in the way the formation of overall satisfaction can be explained. Beyond the pure identification of segmentspecific satisfaction factors, we furthermore show how this fine-granular rating information can be exploited to improve the accuracy of rating-based recommender systems. In particular, we propose to utilize user-and object-specific factor relevance weights which can be learned through linear regression. An empirical evaluation on datasets from different domains finally shows that our method helps us to predict the customer preferences more accurately and thus to develop better online recommendation services.

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“…For instance, by exploiting a set of features for each tourist's specific interaction session, Ricci et al described two case-based reasoning approaches Ricci et al [2006aRicci et al [ , 2006b for travel recommendation and advisory. Mahmood et al [2009] used conversational systems to autonomously improve the recommendation strategy and applied their approach within a prototype of an online travel recommender system. Mahmood et al [2009] used conversational systems to autonomously improve the recommendation strategy and applied their approach within a prototype of an online travel recommender system.…”

Section: Related Workmentioning

confidence: 99%

Object-Oriented Travel Package Recommendation

Tan

Liu

Chen

et al. 2014

ACM Trans. Intell. Syst. Technol.

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Providing better travel services for tourists is one of the important applications in urban computing. Though many recommender systems have been developed for enhancing the quality of travel service, most of them lack a systematic and open framework to dynamically incorporate multiple types of additional context information existing in the tourism domain, such as the travel area, season, and price of travel packages. To that end, in this article, we propose an open framework, the Objected-Oriented Recommender System (ORS), for the developers performing personalized travel package recommendations to tourists. This framework has the ability to import all the available additional context information to the travel package recommendation process in a cost-effective way. Specifically, the different types of additional information are extracted and uniformly represented as feature-value pairs. Then, we define the Object, which is the collection of the feature-value pairs. We propose two models that can be used in the ORS framework for extracting the implicit relationships among Objects. The Objected-Oriented Topic Model (OTM) can extract the topics conditioned on the intrinsic feature-value pairs of the Objects. The Objected-Oriented Bayesian Network (OBN) can effectively infer the cotravel probability of two tourists by calculating the co-occurrence time of feature-value pairs belonging to different kinds of Objects. Based on the relationships mined by OTM or OBN, the recommendation list is generated by the collaborative filtering method. Finally, we evaluate these two models and the ORS framework on real-world travel package data, and the experimental results show that the ORS framework is more flexible in terms of incorporating additional context information, and thus leads to better performances for travel package recommendations. Meanwhile, for feature selection in ORS, we define the feature information entropy, and the experimental results demonstrate that using features with lower entropies usually leads to better recommendation results.

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Improving Recommendation Effectiveness: Adapting a Dialogue Strategy in Online Travel Planning

Cited by 40 publications

References 16 publications

An English-Language Argumentation Interface for Explanation Generation with Markov Decision Processes in the Domain of Academic Advising

An English-Language Argumentation Interface for Explanation Generation with Markov Decision Processes in the Domain of Academic Advising

Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations

Object-Oriented Travel Package Recommendation

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