scholarly journals On the Smaller Number of Inputs for Determining User Preferences in Recommender Systems

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2138
Author(s):  
Sang-Min Choi ◽  
Dongwoo Lee ◽  
Chihyun Park
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Recommendation System ◽  
Magic Number ◽  
User Profile ◽  
User Preferences ◽  
User Preference ◽  
Minimum Number ◽  
Movie Recommendation ◽  
Content Based Filtering

One of the most popular applications for the recommender systems is a movie recommendation system that suggests a few movies to a user based on the user’s preferences. Although there is a wealth of available data on movies, such as their genres, directors and actors, there is little information on a new user, making it hard for the recommender system to suggest what might interest the user. Accordingly, several recommendation services explicitly ask users to evaluate a certain number of movies, which are then used to create a user profile in the system. In general, one can create a better user profile if the user evaluates many movies at the beginning. However, most users do not want to evaluate many movies when they join the service. This motivates us to examine the minimum number of inputs needed to create a reliable user preference. We call this the magic number for determining user preferences. A recommender system based on this magic number can reduce user inconvenience while also making reliable suggestions. Based on user, item and content-based filtering, we calculate the magic number by comparing the accuracy resulting from the use of different numbers for predicting user preferences.

Design and evaluation of a movie recommendation system showing a review for evoking interested

2017 ◽  
Vol 13 (1) ◽  
pp. 72-84 ◽  
Author(s):  
Yuto Ishida ◽  
Takahiro Uchiya ◽  
Ichi Takumi
Keyword(s):  
Recommendation System ◽  
User Profile ◽  
User Preferences ◽  
User Preference ◽  
Item Selection ◽  
Simple Method ◽  
Content Type ◽  
Goods And Services ◽  
Outcomes Evaluation ◽  
Movie Recommendation

Purpose In recent years, e-commerce (EC) sites dealing in various goods and services have increased along with internet popularity. Now, very few EC recommendation systems present a concrete reason for their recommendations. Therefore, because user preferences strongly influence outcomes, evaluation and selection are difficult for items, such as books, movies and luxury goods. The purpose of this paper is evoking interest by showing the review as a reason for a user’s decision-making factor. This paper aims to presents the development and introduction of a recommendation system that presents a review adapted to user preference. Design/methodology/approach The system presents a review to the user, which indicates the reason for matching the item contents and user preferences. Thereby, this system enables the creation of personalized reasons for recommendations. Findings Recommendation sentences conforming to user preferences are effective for item selection. Even with a simple method, in this paper, it was possible to present a review which is an item selection factor sufficient for the user. Originality/value This system can show a recommendation sentence that conforms to a user’s preferences merely from a user profile with the tag data of a product. This paper dealt in movies, but it can easily be applied even for other items.

Toward Geospatial Collaborative Tourism Recommender Systems

2018 ◽  
pp. 212-248
Author(s):  
Zahra Bahramian ◽  
Rahim Ali Abbaspour ◽  
Christophe Claramunt
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
World Wide ◽  
Information Overload ◽  
Spatial Information ◽  
User Profile ◽  
User Preferences ◽  
Research Issues ◽  
The World ◽  
Information Finding

Tourism activities are highly dependent on spatial information. Finding the most interesting travel destinations and attractions and planning a trip are still open research issues to GIScience research applied to the tourism domain. Nowadays, huge amounts of information are available over the world wide web that may be useful in planning a visit to destinations and attractions. However, it is often time consuming for a user to select the most interesting destinations and attractions and plan a trip according to his own preferences. Tourism recommender systems (TRSs) can be used to overcome this information overload problem and to propose items taking into account the user preferences. This chapter reviews related topics in tourism recommender systems including different tourism recommendation approaches and user profile representation methods applied in the tourism domain. The authors illustrate the potential of tourism recommender systems as applied to the tourism domain by the implementation of an illustrative geospatial collaborative recommender system using the Foursquare dataset.

A Particle Swarm Optimization based Hybrid Recommendation System

2016 ◽  
Vol 6 (2) ◽  
pp. 1-10
Author(s):  
Rabi Narayan Behera ◽  
Sujata Dash
Keyword(s):  
Recommendation System ◽  
Optimization Technique ◽  
User Profile ◽  
Matching Algorithm ◽  
Profile Matching ◽  
Machine Learning Approach ◽  
Movie Recommendation ◽  
Hybrid Recommendation ◽  
Content Based Filtering ◽  
Fine Tune

Due to rapid digital explosion user shows interest towards finding suggestions regarding a particular topic before taking any decision. Nowadays, a movie recommendation system is an upcoming area which suggests movies based on user profile. Many researchers working on supervised or semi-supervised ensemble based machine learning approach for matching more appropriate profiles and suggest related movies. In this paper a hybrid recommendation system is proposed which includes both collaborative and content based filtering to design a profile matching algorithm. A nature inspired Particle Swam Optimization technique is applied to fine tune the profile matching algorithm by assigning to multiple agents or particle with some initial random guess. The accuracy of the model will be judged comparing with Genetic algorithm.

scholarly journals Incorporating popularity in a personalized news recommender system

2016 ◽  
Vol 2 ◽  
pp. e63 ◽  
Author(s):  
Nirmal Jonnalagedda ◽  
Susan Gauch ◽  
Kevin Labille ◽  
Sultan Alfarhood
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Recommendation System ◽  
User Profile ◽  
Online News ◽  
User Interests ◽  
News Sources ◽  
Enormous Amount ◽  
News Recommendation

Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily overwhelmed by information of little interest to them. News recommender systems help users manage this flood by recommending articles based on user interests rather than presenting articles in order of their occurrence. We present our research on developing personalized news recommendation system with the help of a popular micro-blogging service, “Twitter.” News articles are ranked based on the popularity of the article identified from Twitter’s public timeline. In addition, users construct profiles based on their interests and news articles are also ranked based on their match to the user profile. By integrating these two approaches, we present a hybrid news recommendation model that recommends interesting news articles to the user based on their popularity as well as their relevance to the user profile.

scholarly journals Book Recommendation System using Matrix Factorization

2021 ◽  
Vol 9 (VI) ◽  
pp. 4578-4582
Author(s):  
K. Venkata Ruchitha
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Previous History ◽  
Speed Up ◽  
Area Unit ◽  
Content Based Filtering

In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.

Recommender System

2017 ◽  
pp. 176-197
Author(s):  
Avinash Navlani ◽  
Nidhi Dadhich
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
Rapid Change ◽  
Computation Time ◽  
User Preferences ◽  
Choice Overload ◽  
Online Systems ◽  
Computing Platform ◽  
Evaluation Of Performance ◽  
Content Based Filtering

With the increase in user choices and rapid change in user preferences, various methods required to capture such increasing choices and changing preferences. Online systems require quick adaptability. Another aspect is that with the increase in a number of items and users, computation time increases considerably. Thus system needs parallel computing platform to run newer designed recommender system techniques. Recommendation system helps people to tackle the choice overload problem and help to select the efficient one. Even though there is lots of work have been done in the recommendation system, still there is a problem in handling various types of data and basically to handle a large amount of data. The main aim of the recommendation system is to provide the best opinion from the available large amount of data. The present chapter describes an introduction to recommender systems, its functions, types, techniques, applications, collaborative filtering, content-based filtering and evaluation of performance.

scholarly journals A Movie Recommendation System Based on A Convolutional Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1455
Author(s):  
Jyoti Kumari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Cosine Similarity ◽  
The Other ◽  
Human Effort ◽  
Movie Recommendation ◽  
Content Based Filtering

Abstract: Due to its vast applications in several sectors, the recommender system has gotten a lot of interest and has been investigated by academics in recent years. The ability to comprehend and apply the context of recommendation requests is critical to the success of any current recommender system. Nowadays, the suggestion system makes it simple to locate the items we require. Movie recommendation systems are intended to assist movie fans by advising which movie to see without needing users to go through the time-consuming and complicated method of selecting a film from a large number of thousands or millions of options. The goal of this research is to reduce human effort by recommending movies based on the user's preferences. This paper introduces a method for a movie recommendation system based on a convolutional neural network with individual features layers of users and movies performed by analyzing user activity and proposing higher-rated films to them. The proposed CNN approach on the MovieLens-1m dataset outperforms the other conventional approaches and gives accurate recommendation results. Keywords: Recommender system, convolutional neural network, movielens-1m, cosine similarity, Collaborative filtering, content-based filtering.

scholarly journals Tangible Cultural Analytics: The Adoption of Recommender Systems Within Cultural Research

2021 ◽  
Author(s):  
Ben Ashley
Keyword(s):  
Augmented Reality ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
User Profile ◽  
Cultural Research ◽  
Augmented Reality Application ◽  
Pros And Cons ◽  
Object Similarity ◽  
Content Based Filtering

The prospect of implementing recommender systems within the context of cultural research has not been explored nearly as much compared to implementation in e-commerce websites and applications. Recommender systems allow for users to be shown new objects either based upon object similarity or based upon what the algorithm thinks the user will like – which can be derived from user feedback and comparing the user to other similar users. This paper discusses how a recommender system could benefit an augmented reality application that enables 3D viewing of artifacts – as part of the Tangible Cultural Analytics (TCA) project at Ryerson University’s Synaesthetic Lab. This paper outlines four recommender systems: 1) content-based filtering, 2) collaborative filtering, 3) cluster models 4) search based models, and 5) hybrid models; discussing the pros and cons to each. Ultimately, a content-based model without the user profile aspect was chosen for this stage in the prototype. This model showed us just how much potential these recommender systems have when helping cultural researchers uncover new relationships and pieces of history through the study and comparison of artifacts.

scholarly journals A CONTEXT-AWARE TOURISM RECOMMENDER SYSTEM BASED ON A SPREADING ACTIVATION METHOD

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 333-339 ◽  
Author(s):  
Z. Bahramian ◽  
R. Ali Abbaspour ◽  
C. Claramunt
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Contextual Information ◽  
User Profile ◽  
User Preferences ◽  
Activation Technique ◽  
Spreading Activation ◽  
Context Aware ◽  
The City

Users planning a trip to a given destination often search for the most appropriate points of interest location, this being a non-straightforward task as the range of information available is very large and not very well structured. The research presented by this paper introduces a context-aware tourism recommender system that overcomes the information overload problem by providing personalized recommendations based on the user’s preferences. It also incorporates contextual information to improve the recommendation process. As previous context-aware tourism recommender systems suffer from a lack of formal definition to represent contextual information and user’s preferences, the proposed system is enhanced using an ontology approach. We also apply a spreading activation technique to contextualize user preferences and learn the user profile dynamically according to the user’s feedback. The proposed method assigns more effect in the spreading process for nodes which their preference values are assigned directly by the user. The results show the overall performance of the proposed context-aware tourism recommender systems by an experimental application to the city of Tehran.

scholarly journals Tangible Cultural Analytics: The Adoption of Recommender Systems Within Cultural Research

2021 ◽  
Author(s):  
Ben Ashley
Keyword(s):  
Augmented Reality ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
User Profile ◽  
Cultural Research ◽  
Augmented Reality Application ◽  
Pros And Cons ◽  
Object Similarity ◽  
Content Based Filtering

The prospect of implementing recommender systems within the context of cultural research has not been explored nearly as much compared to implementation in e-commerce websites and applications. Recommender systems allow for users to be shown new objects either based upon object similarity or based upon what the algorithm thinks the user will like – which can be derived from user feedback and comparing the user to other similar users. This paper discusses how a recommender system could benefit an augmented reality application that enables 3D viewing of artifacts – as part of the Tangible Cultural Analytics (TCA) project at Ryerson University’s Synaesthetic Lab. This paper outlines four recommender systems: 1) content-based filtering, 2) collaborative filtering, 3) cluster models 4) search based models, and 5) hybrid models; discussing the pros and cons to each. Ultimately, a content-based model without the user profile aspect was chosen for this stage in the prototype. This model showed us just how much potential these recommender systems have when helping cultural researchers uncover new relationships and pieces of history through the study and comparison of artifacts.

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