Development of a travel recommender system

Nowadays, e-commerce is facing the problem of information overload, where users are exposed to a vast amount of content, making it more difficult for users to effectively take quality decisions. The need for delivering the right items at the right moment to each user has resulted in making recommendation systems one of the hot topics in research and technology trends, where a good recommendation system might give a key advantage to an e-commerce over its competitors. Industry-leading companies such as Youtube or Amazon introduced the concept of large-scale recommendation systems, where the number of candidate items to consider for recommendation is enormous, and efficient techniques must be applied. The most common way to deal with large-scale recommendation systems nowadays is to build a retrieval model that retrieves a subset of relevant items for the user, and a ranking model that scores and ranks the set of retrieved items. Research on recommendation systems is continuously evolving, where new approaches produce state-of-the-art results, which especially happens thanks to the rise of deep learning. In this thesis, we describe classical and current approaches to recommendation systems, from content-based methods without assuming latent factors and collaborative-filtering methods like matrix factorization, to hybrid approaches, deep learning-based methods, and state-of-the-art approaches. Precisely, we focus on the concept of context-aware methods and multitask methods, which aim to optimize more than one task at a time. In this master thesis, we focus on developing a recommendation system for Stayforlong as a proof of concept. Firstly, we analyze their data and see that we can opt for a hybrid context-aware model since we have at our disposal user features, item features and context features. Another thing that characterizes the data set that we work with is the abundance of implicit feedback and the scarcity of explicit feedback. This drives us to experiment with different model architectures and approaches, focusing on developing a hybrid model that performs a retrieval task and another hybrid model that performs a ranking task. In addition, we check in our experiments the benefits of adding context to our models, the benefits of jointly training a model that optimizes multiple tasks, and the benefits of training a model on an abundant data set, like implicit feedback, and applying transfer learning to fine-tune on explicit feedback the learned representations. Results show that, in rich data scenarios, a context-aware multitask hybrid model trained on implicit feedback and fine-tuned with explicit feedback outperforms other approaches such as training separate retrieval and ranking models, disregarding implicit feedback or not including context features in the models. Finally, we propose as future work a data pipeline for the recommendation system to be used in production, taking into account data freshness and model re-training

Individual and Domain Adaptation in Sentence Planning for Dialogue

One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations

A spiral model for adding automatic, adaptive authoring to adaptive hypermedia

At present a large amount of research exists into the design and implementation of adaptive systems. However, not many target the complex task of authoring in such systems, or their evaluation. In order to tackle these problems, we have looked into the causes of the complexity. Manual annotation has proven to be a bottleneck for authoring of adaptive hypermedia. One such solution is the reuse of automatically generated metadata. In our previous work we have proposed the integration of the generic Adaptive Hypermedia authoring environment, MOT ( My Online Teacher), and a semantic desktop environment, indexed by Beagle++. A prototype, Sesame2MOT Enricher v1, was built based upon this integration approach and evaluated. After the initial evaluations, a web-based prototype was built (web-based Sesame2MOT Enricher v2 application) and integrated in MOT v2, conforming with the findings of the first set of evaluations. This new prototype underwent another evaluation. This paper thus does a synthesis of the approach in general, the initial prototype, with its first evaluations, the improved prototype and the first results from the most recent evaluation round, following the next implementation cycle of the spiral model [Boehm, 88]