An Investigation on the Impact of Natural Language on Conversational Recommendations (original) (raw)

A Domain-independent Framework for building Conversational Recommender Systems

2018

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 contentbased 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 identify...

A Framework for Building Chat-based Recommender Systems

2018

Chat-based recommender systems are getting more and more attention in recent time given their natural interaction with the user. Indeed, chat-based recommender systems implement a paradigm where users define their preferences and discover items that best fit their needs through a dialog. A chat-based recommender system can be easily integrated in platforms such as social networks, e-commerce websites, bank websites. Therefore, the preferences can be directly provided by the users during the dialog or can be automatically extracted from their activities on the same platform that hosts the chatbot [3]. In this demo, we present a framework for building chatbased recommender systems. The framework, based on a content-based recommendation algorithm, is independent from the domain.

FORCE: A Framework of Rule-Based Conversational Recommender System

2022

The conversational recommender systems (CRSs) have received extensive attention in recent years. However, most of the existing works focus on various deep learning models, which are largely limited by the requirement of large-scale human-annotated datasets. Such methods are not able to deal with the cold-start scenarios in industrial products. To alleviate the problem, we propose FORCE, a Framework Of Rule-based Conversational Recommender system that helps developers to quickly build CRS bots by simple configuration. We conduct experiments on two datasets in different languages and domains to verify its effectiveness and usability.

Conversational framework for web search and recommendations

2010

Abstract. In this paper, we describe a Conversational Interaction framework as an innovative and natural approach to facilitate easier information access by combining web search and recommendations. This framework includes an intelligent information agent (Cobot) in the conversation that provides contextually relevant social and web search recommendations.

A Survey on Intelligent Chatbot for Entertainment Recommendation

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Chatbots, also called chatterbots, is a form of artificial intelligence (AI) that simulates text or voice commands and give answers accordingly. It is a computer application uses voice instructions, text dialogues, or both to mimic human communication. In order to reduce the problem of information overload, which has created a possible problem for many Internet users, it is necessary to filter, prioritize, and efficiently distribute essential information on the Internet, where the quantity of options is overwhelming. A chatbot can be employed to communicate with end users, respond to their questions, comprehend their feelings, and make suggestions that are relevant. The objective of our project is to build a chatbot that allows the user to interact with it and get movies or songs recommendation of his liking/choice. Our proposed system is a single page website which can be run on user's desktop as well as mobile and its main focus is to accurately comprehend the user's question in text or voice format and respond to the user with relevant responses. We used Natural Language Processing (NLP) to convert the human conversation in text or voice format into data that is decrypted using recurrent text and patterns and then shaped into automated answers and responses. In recommendation model, Content based filtering is used which works on the data that we take from the user. The system will suggest various movies or songs based on the user's interests, and the result will be shown to the user.