Visual Hybrid Recommendation Systems Based on the Content-Based Filtering

2020 ◽  
pp. 455-465
Author(s):  
Piotr Woldan ◽  
Piotr Duda ◽  
Yoichi Hayashi
Keyword(s):  
Recommendation Systems ◽  
Hybrid Recommendation ◽  
Content Based Filtering

HybRecSys: Content-based contextual hybrid venue recommender system

2018 ◽  
Vol 45 (2) ◽  
pp. 212-226 ◽  
Author(s):  
Aysun Bozanta ◽  
Birgul Kutlu
Keyword(s):  
Recommendation System ◽  
Contextual Information ◽  
Recommendation Systems ◽  
Location Based Services ◽  
Related Information ◽  
Weather Underground ◽  
Information User ◽  
Location Based Social Networks ◽  
Hybrid Recommendation ◽  
Content Based Filtering

The popularity of location-based social networks has prompted researchers to study recommendation systems for location-based services. When used separately, each existing venue recommendation system algorithm has its own drawbacks (e.g. cold start, data sparsity, scalability). Another issue is that critical information about context is not commonly used in venue recommendation systems. This article proposes a hybrid recommendation model that combines contextual information, user-based and item-based collaborative filtering and content-based filtering. For this purpose, we collected user visit histories, venue-related information (distance, category, popularity and price) and contextual information (weather, season, date and time of visits) related to individual user visits from Twitter, Foursquare and Weather Underground. Experimental evaluation of the proposed hybrid system (HybRecSys) using a real-world dataset shows better results than baseline approaches.

scholarly journals Techniques of Recommender System

2019 ◽  
Vol 8 (9S) ◽  
pp. 373-379
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Information Overload ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Data Filtering ◽  
Filtering Technique ◽  
Comprehensive Introduction ◽  
Hybrid Recommendation ◽  
Content Based Filtering

The term Recommender system is described as any organization that provides personalized suggestions as a result and it effects the user in the individualized way to favorable items from the large number of opinions. The voluminous inflation of the reachable data online and also the number of users have lead to the information overload problem. To overcome this problem the recommender system came into play as it is able to prioritize and personalize the data. Recommendation systems have developed alongside with the net. Recommender system has mainly three data filtering methods such as content based filtering technique, collaborative based filtering technique and the hybrid approach to manage the data overload problem and to recommends the items to the user the items they are interested in from the dynamically generated data. This paper makes a comprehensive introduction to the recommender system with its types, content based filtering , collaborative filtering and the hybrid recommendation.

Hybrid Recommendation Systems

2011 ◽  
pp. 311-335
Author(s):  
Konstantinos Markellos ◽  
Penelope Markellou ◽  
Aristotelis Mertis ◽  
Ionna Mousourouli ◽  
Angeliki Panayiotaki ◽  
...  
Keyword(s):  
Recommendation Systems ◽  
Future Trends ◽  
Hybrid Approaches ◽  
Methods And Techniques ◽  
Product Recommendations ◽  
Movie Ratings ◽  
Matching Techniques ◽  
Hybrid Recommendation ◽  
Content Based Filtering ◽  
Cold Start Problem

Recommendation systems have been used in e-commerce sites to make product recommendations and to provide customers with information that helps them decide which product to buy. They are based on different methods and techniques for suggesting products with the most well known being collaborative and content-based filtering. Recently, several recommendation systems adopted hybrid approaches by combining collaborative and content-based features as well as other techniques in order to avoid their limitations. In this chapter, we investigate hybrid recommendations systems and especially the way they support movie e-shops in their attempt to suggest movies to customers. Specifically, we introduce an approach where the knowledge about customers and movies is extracted from usage mining and ontological data in conjunction with customer-movie ratings and matching techniques between customers. This integration provides additional knowledge about customers’ preferences and allows the production of successful recommendations. Even in the case of the cold-start problem where no initial behavioural information is available, the approach can provide logical and relevant recommendations to the customers. The provided recommendations are expected to have higher accuracy in matching customers’ preferences and thus higher acceptance by them. Finally, we describe future trends and challenges and discuss the open issues in the field.

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

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 Personalized Suggestion For Music Based On Collaborative Filtering

2020 ◽  
Vol 9 (05) ◽  
pp. 25047-25051
Author(s):  
Aniket Salunke ◽  
Ruchika Kukreja ◽  
Jayesh Kharche ◽  
Amit Nerurkar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cold Start ◽  
The Internet ◽  
Music Recommendation ◽  
User Similarity ◽  
Content Based Filtering ◽  
Music Recommendation System ◽  
Cold Start Problem

With the advancement of technology there are millions of songs available on the internet and this creates problem for a person to choose from this vast pool of songs. So, there should be some middleman who must do this task on behalf of user and present most relevant songs that perfectly fits the user’s taste. This task is done by recommendation system. Music recommendation system predicts the user liking towards a particular song based on the listening history and profile. Most of the music recommendation system available today will give most recently played song or songs which have overall highest rating as suggestions to users but these suggestions are not personalized. The paper purposes how the recommendation systems can be used to give personalized suggestions to each and every user with the help of collaborative filtering which uses user similarity to give suggestions. The paper aims at implementing this idea and solving the cold start problem using content based filtering at the start.

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