scholarly journals Let´s go to the cinema! A movie recommender system for ephemeral groups of users

2015 ◽  
Author(s):  
Guillermo Fernández ◽  
Waldemar López ◽  
Bruno Rienzi ◽  
Pablo Rodríguez-Bocca
Keyword(s):  
Response Time ◽  
Recommender Systems ◽  
Recommender System ◽  
Mobile Application ◽  
Recommendation System ◽  
Cold Start ◽  
Entire Group ◽  
Group Model ◽  
Social Activities ◽  
User Data

Going to the cinema or watching television are social activities that generally take place in groups. In these cases, a recommender system for ephemeral groups of users is more suitable than (well-studied) recommender systems for individuals. In this paper we present a recommendation system for groups of users that go to the cinema. The system uses the Slope One algorithm for computing individual predictions and the Multiplicative Utilitarian Strategy as a model to make a recommendation to an entire group. We show how we solved all practical aspects of the system; including its architecture and a mobile application for the service, the lack of user data (ramp-up and cold-start problems), the scaling fit of the group model strategy, and other improvements in order to reduce the response time. Finally, we validate the performance of the system with a set of experiments with 57 ephemeral groups.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

scholarly journals An Automated Music Recommendation System Based on Listener Preferences

2021 ◽  
Author(s):  
Mukkamala. S.N.V. Jitendra ◽  
Y. Radhika
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Recommendation System ◽  
Software Tool ◽  
Vital Role ◽  
Music Recommendation ◽  
The World ◽  
Different Types ◽  
Music Recommendation System

Recommender systems play a vital role in e-commerce. It is a big source of a market that brings people from all over the world to a single place. It has become easy to access and reach the market while sitting anywhere. Recommender systems do a major role in the commerce mobility go smoothly easily as it is a software tool that helps in showing or recommending items based on user’s preferences by analyzing their taste. In this paper, we make a recommender system that would be specifically for music applications. Different people listen to different types of music, so we make note of their taste in music and suggest to them the next song based on their previous choice. This is achieved by using a popularity algorithm, classification, and collaborative filtering. Finally, we make a comparison of the built system for its effectiveness with different evaluation metrics.

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.

scholarly journals An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

2019 ◽  
Vol 11 (9) ◽  
pp. 182 ◽  
Author(s):  
Paul Sheridan ◽  
Mikael Onsjö ◽  
Claudia Becerra ◽  
Sergio Jimenez ◽  
George Dueñas
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
System Development ◽  
Cold Start ◽  
Star Trek ◽  
User Preference ◽  
Future Research ◽  
Television Series ◽  
K Nearest Neighbors

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

scholarly journals A METHOD OF COMPUTER SIMULATION MODELING OF USER AND BOT BEHAVIOR IN A RECOMMENDATION SYSTEM USING THE GRAPH DATABASE NEO4J

2021 ◽  
pp. 23-31
Author(s):  
Yelyzaveta Meleshko ◽  
Mykola Yakymenko ◽  
Viktor Bosko
Keyword(s):  
Computer Simulation ◽  
Complex Networks ◽  
Simulation Model ◽  
Recommender Systems ◽  
Recommender System ◽  
Simulation Modeling ◽  
Recommendation System ◽  
Object Oriented Programming ◽  
Computer Simulation Model ◽  
Injection Attacks

The subject matter of the article is the process of computer simulation modeling of complex networks. The goal is to develop a method of computer simulation modeling of ordinary user and bot behavior in a recommendation system based on the theory of complex networks to test the accuracy and robustness of various algorithms for generating recommendations. The tasks to be solved are: to develop a computer simulation model of user and bot behavior in a recommendation system with the ability to generate datasets for testing recommendation generation algorithms. The methods used are: graph theory, theory of complex networks, statistics theory, probability theory, methods of object-oriented programming and methods of working with graph databases. Results. A method of computer simulation modeling of users and objects in a recommender system was proposed, which consists of generating the structure of the social graph of a recommender system and simulating user and bot behavior in it. A series of experiments to test the performance of the developed computer simulation model was carried out. During the experiments, working and testing datasets were generated. Based on the working datasets, the preferences of users by the method of collaborative filtering were predicted. Based on testing datasets, the accuracy of prediction predictions was checked. The results of the experiments showed that the jitter of the investigated values of the Precision, Recall and RMSE of prediction predictions in most practical cases confidently fits within the allowable fluctuation limits, and therefore the users' behavior in computer simulation model was not random and it real users' behavior with certain preferences was simulated. This confirms the reliability of the developed computer simulation model of a recommendation system. Conclusions. A method of computer simulation modeling of user and bot behavior in a recommendation system, which allows generating datasets for testing the algorithms for generating recommendations, was proposed. The developed method makes it possible to simulate the behavior of both ordinary users and bots, which makes it possible to create datasets for testing the robustness of recommender systems to information attacks, as well as for testing the effectiveness of methods for detecting and neutralizing botnets. The structure of relations between users and objects of the recommender system was modeled using the theory of complex networks. Information attacks of bots were modeled on the basis of known models of profile-injection attacks on recommender systems.

Challenges and Applications of Recommender Systems in E-Commerce

2021 ◽  
pp. 175-188
Author(s):  
Taushif Anwar ◽  
V. Uma ◽  
Md Imran Hussain
Keyword(s):  
Recommender Systems ◽  
User Experience ◽  
Recommender System ◽  
Recommendation System ◽  
Main Idea ◽  
Important Task ◽  
Significant Challenge ◽  
Online Business ◽  
The One ◽  
The Relationship

E-commerce and online business are getting too much attention and popularity in this era. A significant challenge is helping a customer through the recommendation of a big list of items to find the one they will like the most efficiently. The most important task of a recommendation system is to improve user experience through the most relevant recommendation of items based on their past behaviour. In e-commerce, the main idea behind the recommender system is to establish the relationship between users and items to recommend the most relevant items to the particular user. Most of the e-commerce websites such as Amazon, Flipkart, E-Bay, etc. are already applying the recommender system to assist their users in finding appropriate items. The main objective of this chapter is to illustrate and examine the issues, attacks, and research applications related to the recommender system.

scholarly journals HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation

2019 ◽  
Vol 33 ◽  
pp. 3830-3837 ◽  
Author(s):  
Liang Hu ◽  
Songlei Jian ◽  
Longbing Cao ◽  
Zhiping Gu ◽  
Qingkui Chen ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Real World ◽  
Cold Start ◽  
Data Sparsity ◽  
Real World Datasets ◽  
Item Item ◽  
Cold Start Problem

Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.

Efficient Mining of Skyrank Items in Recommender Systems

2012 ◽  
Vol 472-475 ◽  
pp. 3450-3454
Author(s):  
Zhen Hua Huang ◽  
Dong Wang ◽  
Sheng Li Sun
Keyword(s):  
Response Time ◽  
Recommender Systems ◽  
Recommender System ◽  
Efficient Algorithm ◽  
Total Response ◽  
Total Response Time ◽  
Theoretical Analyses

Mining of skyrank items has recently received a lot of attention in recommender system community. Literature [3] presents an efficient algorithm ZHYX to produce the skyrank items in one single subspace. However, in multi-user environments, recommender systems generally receive multiple subspace skyrank queries simultaneously. Hence, in this paper, we propose the first efficient sound and complete algorithm, i.e. AMMSSI(Algorithm for Mining Multiple Subsapce Skyrank Items), to markedly reduce the total response time. The detailed theoretical analyses and extensive experiments demonstrate that our proposed algorithm is both efficient and effective.

scholarly journals Blockchain-Secured Recommender System for Special Need Patients Using Deep Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Eric Appiah Mantey ◽  
Conghua Zhou ◽  
Joseph Henry Anajemba ◽  
Izuchukwu M. Okpalaoguchi ◽  
Onyeachonam Dominic-Mario Chiadika
Keyword(s):  
Deep Learning ◽  
Special Needs ◽  
Recommender Systems ◽  
Recommender System ◽  
Data Privacy ◽  
Recommendation System ◽  
Short Term Memory ◽  
Hospital Data ◽  
Patients With Special Needs ◽  
Deep Learning Model

Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on the extreme subtle hospital-patient data. Thus, disregarding the confidentiality of patients with special needs is not an option. In recent times, several proposed techniques failed to cryptographically guarantee the data privacy of the patients with special needs in the diet recommender systems (RSs) deployment. In order to tackle this pitfall, this paper incorporates a blockchain privacy system (BPS) into deep learning for a diet recommendation system for patients with special needs. Our proposed technique allows patients to get notifications about recommended treatments and medications based on their personalized data without revealing their confidential information. Additionally, the paper implemented machine and deep learning algorithms such as RNN, Logistic Regression, MLP, etc., on an Internet of Medical Things (IoMT) dataset acquired via the internet and hospitals that comprises the data of 50 patients with 13 features of various diseases and 1,000 products. The product section has a set of eight features. The IoMT data features were analyzed with BPS and further encoded prior to the application of deep and machine learning-based frameworks. The performance of the different machine and deep learning methods were carried out and the results verify that the long short-term memory (LSTM) technique is more effective than other schemes regarding prediction accuracy, precision, F1-measures, and recall in a secured blockchain privacy system. Results showed that 97.74% accuracy utilizing the LSTM deep learning model was attained. The precision of 98%, recall, and F1-measure of 99% each for the allowed class was also attained. For the disallowed class, the scores were 89, 73, and 80% for precision, recall, and F1-measure, respectively. The performance of our proposed BPS is subdivided into two categories: the secured communication channel of the recommendation system and an enhanced deep learning approach using health base medical dataset that spontaneously identifies what food a patient with special needs should have based on their disease and certain features including gender, weight, age, etc. The proposed system is outstanding as none of the earlier revised works of literature described a recommender system of this kind.

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