1,727 research outputs found
A domain-independent framework for building conversational recommender systems
Conversational Recommender Systems (CoRSs) implement a paradigm where users can interact with the system for defining their preferences and discovering items that best fit their needs. A CoRS can be straightforwardly implemented as a chatbot. Chatbots are becoming more and more popular for several applications like customer care, health care, medical diagnoses. In the most complex form, the implementation of a chatbot is a challenging task since it requires knowledge about natural language processing, human-computer interaction, and so on. In this paper, we propose a general framework for making easy the generation of conversational recommender systems. The framework, based on a content-based recommendation algorithm, is independent from the domain. Indeed, it allows to build a conversational recommender system with different interaction modes (natural language, buttons, hybrid) for any domain. The framework has been evaluated on two state-of-the-art datasets with the aim of identifying the components that mainly influence the final recommendation accuracy
Evaluating Conversational Recommender Systems: A Landscape of Research
Conversational recommender systems aim to interactively support online users
in their information search and decision-making processes in an intuitive way.
With the latest advances in voice-controlled devices, natural language
processing, and AI in general, such systems received increased attention in
recent years. Technically, conversational recommenders are usually complex
multi-component applications and often consist of multiple machine learning
models and a natural language user interface. Evaluating such a complex system
in a holistic way can therefore be challenging, as it requires (i) the
assessment of the quality of the different learning components, and (ii) the
quality perception of the system as a whole by users. Thus, a mixed methods
approach is often required, which may combine objective (computational) and
subjective (perception-oriented) evaluation techniques. In this paper, we
review common evaluation approaches for conversational recommender systems,
identify possible limitations, and outline future directions towards more
holistic evaluation practices
An investigation on the impact of natural language on conversational recommendations
In this paper, we investigate the combination of Virtual Assistants and Conversational Recommender Systems (CoRSs) by designing and implementing a framework named ConveRSE, for building chatbots that can recommend items from different domains and interact with the user through natural language. An user experiment was carried out to understand how natural language influences both the cost of interaction and recommendation accuracy of a CoRS. Experimental results show that natural language can indeed improve user experience, but some critical aspects of the interaction should be mitigated appropriately
A Conversational Movie Recommender System
Master's thesis in Electrical and Computer EngineeringThe purpose of a Conversational Recommender System is to help the users achieve their recommendation specific goals using a multi-turn dialogue. In recent years, numerous studies are conducted on improving the quality attributes of a conversational recommender system. Multiple conversational movie recommender systems are proposed. However, there is a need for a conversational system for a movie recommendation, which can be used for research purposes.
The main goal of this thesis is to create Jarvis, an open-source, rule-based conversational movie recommendation system focusing on understanding the users' goals and adapting to their changing requirements. In order to understand the users' goals, a database is created, which contains the attributes with higher coverage of possible users' goals. A multi-model chat interface is designed for Jarvis. This interface introduces the components for better user interaction and providing users a guide during the conversation.
The success of a conversational system is measured in terms of the quality of the conversation and the satisfaction of the users. To guarantee the success of Jarvis, the conversation of the system with different users is recorded. Moreover, the users are requested to rate their conversation and give feedback about the system. The behavior of the system during the conversation and user feedback is studied to improve Jarvis.
The results have shown that conversational data and users' feedback plays an essential role in improving the performance of Jarvis. The users' satisfaction has improved, and the system adapts better to the previously unknown scenarios in the conversation. However, to make the system more adjustable and user-friendly, more users are required to test the system.submittedVersio
Chatbots for Modelling, Modelling of Chatbots
Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
Evaluating the effectiveness of explanations for recommender systems : Methodological issues and empirical studies on the impact of personalization
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