scholarly journals IMPROVEMENT OF SVD ALGORITHM TO INCREASE THE EFFICIENCY OF RECOMMENDATION SYSTEMS

2021 ◽  
Vol 5 (4) ◽  
pp. 55-59
Author(s):  
Svitlana Krepych ◽  
Iryna Spivak
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
User Preferences ◽  
Automated Generation ◽  
Resource Information ◽  
Web Resource ◽  
Internet Resources ◽  
The People ◽  
Static Information ◽  
System Data

Many existing websites use recommendation systems for their users. They generate various offers for them, for example, similar products or recommend the people registered on this site with similar interests. Such referral mechanisms process vast amounts of information to identify potential user preferences. Recommendation systems are programs that try to determine what users want to find, what might interest them, and recommend it to them. These mechanisms have improved the interaction between the user and the site. Instead of static information, they provide dynamic information that changes: recommendations are generated separately for each user, based on his previous activity on this web resource. Information from other visitors may also be taken into account. The methods of collecting information provided by the Internet have greatly simplified the use of human thought through collaborative filtering. But, on the other hand, the large amount of information complicates the implementation of this possibility. For example, the behavior of some people is quite clearly amenable to modeling, while others behave completely unpredictably. And it is the latter that affect the shift of the results of the recommendation system and reduce its effectiveness. An analysis of Internet resources has shown that most of the recommendation systems do not provide recommendations to users, and the part that does, for example, offers products to the user, selects recommendations manually. Therefore, the task of developing methods for automated generation of recommendations for a limited set of input data is quite relevant. The problems of data sparseness, new user problem, scalability of the widely used SVD algorithm for the development of such recommendation systems are proposed to be eliminated by improving this algorithm by the method of the nearest k-neighbors. This method will allow you to easily segment and cluster system data, which will save system resources.

A Survey on Recent Recommendation Systems for the Tourism Industry

2020 ◽  
pp. 205-237
Author(s):  
S. Ranjith ◽  
P. Victer Paul
Keyword(s):  
User Satisfaction ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Tourism Industry ◽  
User Preferences ◽  
Reference List ◽  
Current State ◽  
Recent Recommendation ◽  
Important Field ◽  
Preference Criteria

Data mining is an important field that derives insights from the data and recommendation systems. Recommendation systems have become common in recent years in the field of tourism. These are widely used as a tool that can input various selection criteria and user preferences and yields travel recommendations to tourists. User's style and preferences should be constructed accurately so as to supply most relevant suggestions. Researchers proposed various types of tourism recommendation systems (TRS) in order to improve the accuracy and user satisfaction. In this chapter, the authors studied the current state of tourism recommendation system models and discussed their preference criteria. As a part of that, the authors studied various important preference factors in TRS and categorized them based on their likeness. This chapter reports TRS model future directions and compiles a comprehensive reference list to assist researchers.

scholarly journals Hybrid POI Recommendation System for Tourism

2018 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Monishkanna Barathan ◽  
Ershad Sharifahmadian
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
User Interaction ◽  
User Preferences ◽  
Knowledge Based ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Information Finding ◽  
Hybrid Recommender ◽  
Available Information

Due to the increase in amount of available information, finding places and planning of the activities to be done during a tour can be strenuous. Tourists are looking for information about a place in which they have not been before, which worsen the selection of places that fit better with user’s preferences. Recommendation systems have been fundamentally applicable in tourism, suggest suitable places, and effectively prune large information from different locations, so tourists are directed toward those places where are matched with their needs and preferences. Several techniques have been studied for point-of-interest (POI) recommendation, including content-based which builds based on user preferences, collaborative filtering which exploits the behavior of other users, and different places, knowledge-based method, and several other techniques. These methods are vulnerable to some limitations and shortcomings related to recommendation environment such as scalability, sparsity, first-rater or gray sheep problems. This paper tries to identify the drawbacks that prevent wide spread use of these methodologies in recommendation. To improve performance of recommendation systems, these methods are combined to form hybrid recommenders. This paper proposes a novel hybrid recommender system which suggests tourism destinations to a user with minimal user interaction. Furthermore, we use sentiment analysis of user’s comments to enhance the efficiency of the proposed system.

Meta-Rule Based Recommender Systems for Educational Applications

2012 ◽  
pp. 211-231 ◽  
Author(s):  
Vicente Arturo Romero Zaldivar ◽  
Daniel Burgos ◽  
Abelardo Pardo
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Relevant Information ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Rule Based ◽  
User Data ◽  
Rule Sets ◽  
Rule Set ◽  
Educational Applications

Recommendation Systems are central in current applications to help the user find relevant information spread in large amounts of data. Most Recommendation Systems are more effective when huge amounts of user data are available. Educational applications are not popular enough to generate large amount of data. In this context, rule-based Recommendation Systems seem a better solution. Rules can offer specific recommendations with even no usage information. However, large rule-sets are hard to maintain, reengineer, and adapt to user preferences. Meta-rules can generalize a rule-set which provides bases for adaptation. In this chapter, the authors present the benefits of meta-rules, implemented as part of Meta-Mender, a meta-rule based Recommendation System. This is an effective solution to provide a personalized recommendation to the learner, and constitutes a new approach to Recommendation Systems.

scholarly journals Smart Recommendation System for Off-the Shelf Medicines

2018 ◽  
Vol 7 (2.24) ◽  
pp. 417
Author(s):  
Ratna Sathappan ◽  
Tholu Sai Indira ◽  
A Meenapriyadarsini
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Opinion Mining ◽  
Recommendation Systems ◽  
Product Category ◽  
Machine Learning Algorithms ◽  
Patient Feedback ◽  
The People ◽  
Using Data ◽  
The Impact

Internet usage has been at an all-time high from 2000’s vintage years. The people who have access to the internet use it for numerous reasons such as social networking, marketing, promoting, enhancing businesses, consultancy, research, gaming and the list goes on. In the recent years, Review websites have flourished, where people share their opinion about a product, with an increase in response rate and reliability. Recommendations are made by mining data from review websites. Traditional Recommendation systems are limited as they only consider certain metrics, such as product purchase details, product category. Recommendation systems are yet to gain popularity in the medical field. These days most patients are unable to figure out the medication that works in healing them in the best way possible, hence they turn to review websites in order to obtain a second opinion on the prescribed medication. In this work, we have developed a smart recommendation system for off-the Shelf Medical Drugs using machine learning and data analytics based on patient feedback. The patient feedback is unstructured data which is processed using data analytic tools. After which machine learning is used to recommend the best fit and compare the drugs. In this work, we predict the impact of a drug/ medicine on the patient to whom the medication was prescribed, using data mining techniques. Firstly, we detect the user’s polarity (positive/ negative/neutral) based on the patient feedback for a certain drug using sentiment analysis and opinion mining following which we use machine learning algorithms to track sentiment variation and to make a recommendation based on user polarity

scholarly journals A Personalized Location Recommendation System based on Probability and Proximity

2019 ◽  
Vol 8 (2) ◽  
pp. 714-718
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Recommendation System ◽  
Predictive Analytics ◽  
Information Filtering ◽  
Recommendation Systems ◽  
User Preferences ◽  
Decision Making Process ◽  
Location Recommendation ◽  
Short Period

Big data is an emerging field plays a valuable role in the extraction of information from raw data. It has found its applications in areas such as predictive analytics, healthcare analytics, financial analytics, and retail analytics and so on. The enormous growth of the Internet has become a source for availability of the huge volume of data online. It is difficult to find out the necessary information from huge data within a short period. The availability of enormous data craves the need of an information filtering system and this information filtering systems are capable of providing the required data to users. The rapid growth of big data lays the path to recommendation systems. A recommendation system is an information filtering tool which has more impacts in day to day life of everyone and also redefines our lives. Recommendation systems provide suggestions based on user preferences, requirements, and interests. The reviews and rating values given by people are used to answer similar interest queries with predictions and suggestions. Reviews and Feedback play a key role in the decision-making process. People share their experiences in the form of feedback, ratings, and reviews and so on. If a user wants to visit a location and if he does not have prior knowledge of it, then he may use reviews and feedback given by others who visited the location already. It is not possible for a user to go through huge volumes of reviews and sometimes it may mislead the user to take wrong decisions if he goes by the review given by a person with a contrasting taste. In such cases, Recommendation systems are needed, which helps users in the decision-making process. In most of the existing methods, they used Point of Interest (POI) of users to recommend the locations. The main objective is to develop a Personalized Location Recommendation System, which will recommend the locations to users using Probability and Proximity. Our model uses Probability and Proximity measures to recommend the locations.

scholarly journals Web crawling based context aware recommender system using optimized deep recurrent neural network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Venugopal Boppana ◽  
P. Sandhya
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recommender System ◽  
Recommendation System ◽  
Contextual Information ◽  
Recommendation Systems ◽  
User Preferences ◽  
Bald Eagle ◽  
Context Aware ◽  
Deep Recurrent Neural Network

AbstractRecommendation systems are obtaining more attention in various application fields especially e-commerce, social networks and tourism etc. The top items are recommended based on the ability of recommender system which predict the future preference out of the available items. Because of the internet, the people in the current society has too many options that’s why the recommendation system is very essential. The recommendation is achieved by the particular users who predict the ratings for numerous items and recommend those items to other users. Majorly, content and collaborative filtering techniques are employed in typical recommendation systems to find user preferences and provide final recommendations. But, these systems commonly lacks to take growing user preferences in various contextual factors. Context aware recommendation systems consider various contextual parameters into account and attempt to catch user preferences appropriately. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. Therefore, in this paper an effective deep learning based context aware recommendation model is proposed which can be act as an efficient recommender system by showing minimum error during recommendation. Initially, the dataset is pre-processed using Natural Language Tool Kit (NLTK) in Python platform. After pre-processing, the TF–IDF and word embedding model is used for every pre-processed reviews to extract the features and contextual information. The extracted feature is considered as an input of density based clustering to group the negative, neutral and positive sentiments of user reviews. Finally, deep recurrent neural Network (DRNN) is employed to get the most preferable user from every cluster. The recurrent neural network model parameter values are initialized through the fitness computation of Bald Eagle Search (BES) algorithm. The proposed model is implemented using NYC Restaurant Rich Dataset using Python programming platform and performance is evaluated based on the metrics of accuracy, precision, recall and compared with existing models. The proposed recommendation model achieves 99.6% accuracy which is comparatively higher than other machine learning models.

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.

scholarly journals A survey of aerosol exposure systems relative to the analysis of cytotoxicity: A Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA) perspective

2021 ◽  
Vol 5 ◽  
pp. 239784732110222
Author(s):  
David Thorne ◽  
Roman Wieczorek ◽  
Toshiro Fukushima ◽  
Han-Jae Shin ◽  
Robert Leverette ◽  
...  
Keyword(s):  
Scientific Research ◽  
Toxicity Testing ◽  
User Preferences ◽  
In Vitro Toxicity ◽  
Aerosol Generation ◽  
Aerosol Exposure ◽  
Comprehensive Survey ◽  
Survey Results ◽  
System Data

During a Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA) meeting, the in vitro toxicity testing Sub-Group (IVT SG) met to discuss the evolving field of aerosol exposure research. Given the diversity of exposure parameters and biological endpoints being used, it was considered a high priority to investigate and contextualise the responses obtained. This is particularly driven by the inability to compare between studies on different exposure systems due to user preferences and protocol differences. Twelve global tobacco and contract research companies met to discuss this topic and formulate an aligned approach on how this diverging field of research could be appropriately compared. Something that is becoming increasingly important, especially in the light of more focused regulatory scrutiny. A detailed and comprehensive survey was conducted on over 40 parameters ranging from aerosol generation, dilution and data analysis across eight geographically independent laboratories. The survey results emphasise the diversity of in vitro exposure parameters and methodologies employed across the IVT SG and highlighted pockets of harmonisation. For example, many of the biological protocol parameters are consistent across the Sub-Group. However, variables such as cell type and exposure time remain largely inconsistent. The next steps for this work will be to map parameters and system data against biological findings and investigate whether the observed inconsistencies translate into increased biological variability. The results from the survey provide improved awareness of parameters and nuances, that may be of substantial benefit to scientists in intersecting fields and in the development of harmonised approaches.

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.

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

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