scholarly journals Text Based Restaurant Recommendation System using End-To-End Memory Network

2020 ◽  
Vol 9 (3) ◽  
pp. 41-45
Keyword(s):  
Recommendation System ◽  
Online Shopping ◽  
Recommendation Systems ◽  
User Needs ◽  
Textual Data ◽  
Memory Network ◽  
End To End ◽  
Network Mechanism ◽  
The Web

With growing use of online content streaming websites, online shopping, and other exclusively online services, it becomes more and more imperative for technology companies to invest a lot of funds into a system to gauge user needs and requirements. To bridge this gap, there has been an influx of recommendation systems in the markets. From advertisements, to movies, and products we buy, recommendation engines are feeding on new data everyday to learn user trends. This paper tries to focus on improving the text based recommendation systems that can be implemented to leverage the vast review data that can be found on websites. We suggest using a novel memory based end-to-end network mechanism to reduce the need for long term dependencies and to reduce the need for memory intensive systems. As we generate more and more reviews and textual data on the web everyday, we need to be able to use this data to make meaningful analytical and business predictions. With the ability to perform multiple lookups, implement attention mechanism and back-propogation, this system was found to perform much better when compared to CNN, RNN and LSTM alternatives in our testing.

A Movie E-Shop Recommendation Model Based on Web Usage and Ontological Data

2011 ◽  
pp. 51-68
Author(s):  
Andreas Aresti ◽  
Penelope Markellou ◽  
Ioanna Mousourouli ◽  
Spiros Sirmakessis ◽  
Athanasios Tsakalidis
Keyword(s):  
Artificial Intelligence ◽  
User Modeling ◽  
Web Mining ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Model Based ◽  
Web Usage ◽  
One To One ◽  
Online Shops

Recommendation systems are special personalization tools that help users to find interesting information and services in complex online shops. Even though today’s e-commerce environments have drastically evolved and now incorporate techniques from other domains and application areas such as Web mining, semantics, artificial intelligence, user modeling, and profiling setting up a successful recommendation system is not a trivial or straightforward task. This chapter argues that by monitoring, analyzing, and understanding the behavior of customers, their demographics, opinions, preferences, and history, as well as taking into consideration the specific e-shop ontology and by applying Web mining techniques, the effectiveness of produced recommendations can be significantly improved. In this way, the e-shop may upgrade users’ interaction, increase its usability, convert users to buyers, retain current customers, and establish long-term and loyal one-to-one relationships.

An Analysis on E-Learning and Its Recommendations

2021 ◽  
pp. 166-187
Author(s):  
Lalitha T. B. ◽  
Sreeja P. S.
Keyword(s):  
Personal Growth ◽  
Recommendation System ◽  
Professional Competence ◽  
Holistic Approach ◽  
Global Village ◽  
Approach To Learning ◽  
E Learning ◽  
Minimal Effort ◽  
The Web

Education provides a predominant source of worldly knowledge around us and changes the perspective of the living society as a global village. However, education has revealed fragmentary remains in the professional competence and personal growth of the learners without the involvement of online learning. E-learning brings out a broader vision of sources to the learners available over the web with the holistic approach to learning from anywhere without cost and minimal effort. The proposed theoretical framework analyses the long-term evolution of e-learning and its effect on mankind. The various methods, technologies, and approaches of e-learning that exist in various forms were discussed exponentially according to the range of necessities among the learners. The recommendation system plays a pivotal role in referring contents and enhancing the learning environment. The education promoted to the learners through the recommendations system over their personal preferences were explored here in detail.

scholarly journals Enabling “Untact” Culture via Online Product Recommendations: An Optimized Graph-CNN based Approach

2020 ◽  
Vol 10 (16) ◽  
pp. 5445
Author(s):  
Wafa Shafqat ◽  
Yung-Cheol Byun
Keyword(s):  
Recommendation System ◽  
Online Shopping ◽  
Service Providers ◽  
Recommendation Systems ◽  
Peak Time ◽  
Optimization Strategy ◽  
Cultural Patterns ◽  
Prediction Rate ◽  
Use Of Technology ◽  
Product Recommendations

The COVID-19 pandemic is swiftly changing our behaviors toward online channels across the globe. Cultural patterns of working, thinking, shopping, and use of technology are changing accordingly. Customers are seeking convenience in online shopping. It is the peak time to assist the digital marketplace with right kind of tools and technologies that uses the strategy of click and collect. Session-based recommendation systems have the potential to be equally useful for both the customers and the service providers. These frameworks can foresee customer’s inclinations and interests, by investigating authentic information on their conduct and activities. Various methods exist and are pertinent in various situations. We propose a product recommendation system that uses a graph convolutional neural network (GCN)-based approach to recommend products to users by analyzing their previous interactions. Unlike other conventional techniques, GCN is not widely explored in recommendation systems. Therefore, we propose a variation of GCN that uses optimization strategy for better representation of graphs. Our model uses session-based data to generate patterns. The input patterns are encoded and passed to embedding layer. GCN uses the session graphs as input. The experiments on data show that the optimized GCN (OpGCN) was able to achieve higher prediction rate with around 93% accuracy as compared with simple GCN (around 88%).

Surgical management of extracranial carotid artery aneurysms

VASA ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 223-228 ◽  
Author(s):  
Jan Paweł Skóra ◽  
Jacek Kurcz ◽  
Krzysztof Korta ◽  
Przemysław Szyber ◽  
Tadeusz Andrzej Dorobisz ◽  
...  
Keyword(s):  
Carotid Artery ◽  
Surgical Management ◽  
Minor Stroke ◽  
Stroke Rate ◽  
Extracranial Carotid Artery ◽  
Long Term Follow Up ◽  
End To End ◽  
Dacron Patch ◽  
Major Stroke

Abstract. Background: We present the methods and results of the surgical management of extracranial carotid artery aneurysms (ECCA). Postoperative complications including early and late neurological events were analysed. Correlation between reconstruction techniques and morphology of ECCA was assessed in this retrospective study. Patients and methods: In total, 32 reconstructions of ECCA were performed in 31 symptomatic patients with a mean age of 59.2 (range 33 - 84) years. The causes of ECCA were divided among atherosclerosis (n = 25; 78.1 %), previous carotid endarterectomy with Dacron patch (n = 4; 12.5 %), iatrogenic injury (n = 2; 6.3 %) and infection (n = 1; 3.1 %). In 23 cases, intervention consisted of carotid bypass. Aneurysmectomy with end-to-end suture was performed in 4 cases. Aneurysmal resection with patching was done in 2 cases and aneurysmorrhaphy without patching in another 2 cases. In 1 case, ligature of the internal carotid artery (ICA) was required. Results: Technical success defined as the preservation of ICA patency was achieved in 31 cases (96.9 %). There was one perioperative death due to major stroke (3.1 %). Two cases of minor stroke occurred in the 30-day observation period (6.3 %). Three patients had a transient hypoglossal nerve palsy that subsided spontaneously (9.4 %). At a mean long-term follow-up of 68 months, there were no major or minor ipsilateral strokes or surgery-related deaths reported. In all 30 surviving patients (96.9 %), long-term clinical outcomes were free from ipsilateral neurological symptoms. Conclusions: Open surgery is a relatively safe method in the therapy of ECCA. Surgical repair of ECCAs can be associated with an acceptable major stroke rate and moderate minor stroke rate. Complication-free long-term outcomes can be achieved in as many as 96.9 % of patients. Aneurysmectomy with end-to-end anastomosis or bypass surgery can be implemented during open repair of ECCA.

Exploring User Information Needs in Online Pet Adoption Profiles

2020 ◽  
Vol 64 (1) ◽  
pp. 1308-1312
Author(s):  
Zoe M. Becerra ◽  
Sweta Parmar ◽  
Keenan May ◽  
Rachel E. Stuck
Keyword(s):  
Information Needs ◽  
Online Shopping ◽  
Physical Characteristics ◽  
User Needs ◽  
User Group ◽  
Animal Shelters ◽  
Types Of Information ◽  
The Right ◽  
And Behavior ◽  
Level Of Importance

With the increase of online shopping, animal shelters can use websites to allow potential adopters to view adoptable animals and increase the number of adoptions. However, little research has evaluated the information needs of this user group. This study conducted a user needs analysis to determine the types of information potential adopters want when searching for a new pet, specifically a cat or dog. Twenty-six participants ranked different behavioral and physical characteristics based on the level of importance and identified their top five overall characteristics. In general, cat adopters ranked the cat’s personality and behavior to be very important and dog adopters found physical characteristics highly important. This study shows the importance of understanding potential adopters’ needs to provide relevant and valued information on online pet adoption profiles. The recommendations and insights can be used to develop pet profiles that meet adopters’ needs and help adopters find the right pet.

scholarly journals Fashion Recommendation Systems, Models and Methods: A Review

Informatics ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 49
Author(s):  
Samit Chakraborty ◽  
Md. Saiful Hoque ◽  
Naimur Rahman Jeem ◽  
Manik Chandra Biswas ◽  
Deepayan Bardhan ◽  
...  
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Fast Fashion ◽  
Potential Models ◽  
Scholarly Article ◽  
Enormous Amount ◽  
Shopping Experience ◽  
Tremendous Amount ◽  
Systems Models ◽  
Filtering Techniques

In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.

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.

Semantic Web mining for Content-Based Online Shopping Recommender Systems

2019 ◽  
Vol 15 (4) ◽  
pp. 41-56 ◽  
Author(s):  
Ibukun Tolulope Afolabi ◽  
Opeyemi Samuel Makinde ◽  
Olufunke Oyejoke Oladipupo
Keyword(s):  
Semantic Web ◽  
Recommender Systems ◽  
Web Mining ◽  
Online Shopping ◽  
Semantic Analysis ◽  
Second Phase ◽  
Web Content ◽  
Textual Data ◽  
Content Mining ◽  
Bayes Algorithm

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

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