A Survey on Intelligent Chatbot for Entertainment Recommendation (original) (raw)

Movie recommender chatbot based on Dialogflow

International Journal of Electrical and Computer Engineering (IJECE)

Currently, the online movie streaming business is growing rapidly, such as Netflix, Disney+, Amazon Prime Video, HBO, and Apple TV. The recommender system helps customers in getting information about movies that are in accordance with their wishes. Meanwhile, the development of messaging platform technology has made it easier for many people to communicate instantly. Utilizing a messaging platform to build a recommender system for movies, provides special benefits because people often access the messaging platform all the time. In the Indonesian language, there are many slang terms that the system must recognize. In this study, we build a chatbot on a messaging platform which users can interact with the system in natural language (in Indonesian language) and get recommendations. We use rule-based and maximum likelihood as a method in natural language processing (NLP), and content-based filtering for the recommendation process. The recommender system interaction is built through a co...

Music Recommender System Using ChatBot

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2021

In this era of technological advances, text-based music recommendations are much needed as they will help humans relieve stress with soothing music according to their moods. In this project, we have implemented a chatbot that recommends music based on the user's text tone. By analyzing the tone of the text expressed by the user, we can identify the mood. Once the mood is identified, the application will play songs in the form of a web page based on the user's choice as well as his current mood. In our proposed system, the main goal is to reliably determine a user's mood based on their text tone with an application that can be installed on the user's desktop. In today's world, human computer interaction (HCI) plays a crucial role, and the most popular concept in HCI is recognition of emotion from text. As part of this process, the frontal view of the user's text is used to determine the mood. The extraction of text tone from the user's text is another important aspect. We have used IBM Analyser to check the text tone of the user and to predict the mood based on the text of the user, and Last.FM API to recommend songs based on the mood of the user.

AI in Entertainment -Movie Recommendation System

IRJET, 2022

Artificial intelligence is now a critical component of a wide range of enterprises. Especially during a pandemic, when cinemas are closed and people working from home have to entertain themselves by viewing movies and listening to music, these people are turning to OTT platforms. On-demand streaming services provide nearly limitless content, and the introduction of JIO fibre, which provides high-speed data at low prices, as well as products like Amazon Fire Stick and Google Chrome-cast, which have transformed traditional television into a smart television, has fundamentally changed the landscape. These OTT services are a library of related content with unlimited debate subjects. One of the most important areas that have to be improved is personalization because it allows each member to have a unique perspective of material that adapts to their interests and can help them broaden their interests over time. Each experience is tailored in a variety of ways, including the films recommended and ranked, the way movies are structured into rows and pages, and even the artwork presented. To achieve this level of deep customisation, we must mix various computational approaches to satisfy the needs of each member. Personalization begins on the site and continues throughout the product and beyond, including selecting what messages to send consumers to keep them informed and engaged. Users should spend less time looking for material to watch and more time watching stuff that will provide actual value to their lives.

Intelligent Chatbot-LDA Recommender System

International Journal of Emerging Technologies in Learning (iJET)

With the proliferation of distance platforms, in particular that of an open access such as Massive Online Open Courses (MOOC), the learner finds himself overwhelmed with data which are not all efficient for his interest. Besides, the MOOC has tools that allow learners to seek information, express their ideas, and participate in discussions in an online forum. This tool is a huge repository of rich data, which continues to evolve, however its exploitation is fiddly in the search for information relevant to the learner. Similarly, the task of the tutor seems to be difficult in management of a large number of learners. To this end, the development of a Chatbot able to meet the requests of learners in a natural language is necessary to the deroulement a course in the MOOC. The ChatBot plays the role of assistant and guide for the learners and for the tutors. However, ChatBot responses come from a knowledge base, which must be relevant. Knowledge extraction to answer questions is a diffi...

IAI MovieBot

Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020

IJERT-Chatbot with Music and Movie Recommendation based on Mood

International Journal of Engineering Research and Technology (IJERT), 2020

https://www.ijert.org/chatbot-with-music-and-movie-recommendation-based-on-mood https://www.ijert.org/research/chatbot-with-music-and-movie-recommendation-based-on-mood-IJERTCONV8IS15025.pdf In this era of technological advancements, music recommendation based on mood is much needed as it will help humans relieve stress and listen to soothing music according to their mood. In this project, we have implemented a chatbot that recommends music as well as movies based on the user's mood. The objective of our application is to identify the mood expressed by the user and once the mood is identified, songs are played by the application or a list of movies are displayed in the form of a website according to the choice made by the user and also his current mood. Our proposed system is implemented as an application which can be run on the user's desktop and its main focus is to reliably determine the user's mood. Human computer interaction (HCI) has a lot of importance in today's world and the most popular concept in HCI is recognition of emotion from facial images. In this process, the frontal view of the facial images is utilized so as to detect the mood from the images. Another important factor is the extraction of facial elements from the user's face. We have used the Haar Cascade Algorithm for accurately detecting the user's face in the live webcam feed and the CNN Algorithm is used to detect the emotion being expressed by the user from the facial features. Facial attributes like the arrangement of the mouth and eyes are used in order to detect the mood of the user.

AUTOMATED CHATBOT IMPLEMENTED USING NATURAL LANGUAGE PROCESSING

International Research Journal of Modernization in Engineering Technology and Science, 2020

In this paper we focus on, providing a Chatbot that will see to all our queries and will provide a solution or answer to that. Usually, companies will be having a backend team who will be answering the customer's questions. This is generally a time consuming and tedious job to be done. For solving these problems, Chatbot was created. Generally, the frequently asked customer questions corresponding answers are stored in a text file. So in this model, it will take the customer's question as input, pre-processing them using some Natural Language Processing techniques that include Tokenization, Lemmatization, and stemming, find the cosine similarity between the question and answers, and provides a score for each answer, and the answer with more score will be considered as the answer for the given question. The answer text file varies from company to company since questions can vary between companies based on the different products available. Hence, the main purpose of the Chatbot is to provide high accuracy by proving the correct and satisfying answer to the customer's question for a company. This paper will be useful to all the MultiNational Companies, by proving a Chatbot model that would output accurate and satisfying answers for the questions asked by their renowned customers.

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.