Content boosted hybrid filtering for solving pessimistic user problem in recommendation systems

2020 ◽  
Vol 24 (6) ◽  
pp. 1477-1496
Author(s):  
Rajalakshmi Sivanaiah ◽  
R. Sakaya Milton ◽  
T.T. Mirnalinee
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Historical Data ◽  
Predictive Accuracy ◽  
Recommendation Systems ◽  
Hybrid Filtering ◽  
Data Content ◽  
Filtering Technique ◽  
Content Based Filtering ◽  
Better Than

The main goal of a recommendation system is to recommend items of interest to users by analyzing their historical data. Content-based and collaborative filtering are the traditional recommendation strategies, each with its own strengths and weaknesses. Some of their weaknesses can be overcome by combining the two strategies. The resulting hybrid system performs qualitatively better than the traditional recommendation systems. However, historical data of some users may consist largely of only likes or only dislikes. Those users are termed as optimistic or pessimistic users respectively. On an average there are around 10 to 20% of pessimistic users present in a given dataset. For pessimistic users, whose profiles have mostly dislikes and very few likes, content-based filtering can hardly recommend any items of interest. In content-based filtering technique pessimistic users get poor recommendations of either uninteresting movies or no recommendations at all. This can be alleviated by boosting the content profiles of pessimistic users using the top-n recommendations of collaborative filtering. This content boosted hybrid filtering system provides a novel list of recommendations even for pessimistic users, with predictive accuracy better than that of a traditional content-based filtering system.

scholarly journals Hybrid Filtering Recommendation System in an Educational Context

2022 ◽  
Vol 17 (1) ◽  
pp. 0-0
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Educational Context ◽  
Hybrid Filtering ◽  
Content Based Filtering ◽  
Research Studies

In education, the needs of learners are different in the majority of the time, as each has specificities in terms of preferences, performance and goals. Recommendation systems have proven to be an effective way to ensure this learning personalization. Already used and tested in other areas such as e-commerce, their adaptation to the educational context has led to several research studies that have tried to find the best approaches with the best expected results. This article suggests that a hybridization of recommendation systems filtering methods can improve the quality of recommendations. An experiment was conducted to test an approach that combines content-based filtering and collaborative filtering. The results proved to be convincing.

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 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.

scholarly journals Movie Recommending System Using Collaborative Filtering

2021 ◽  
Vol 9 (VII) ◽  
pp. 1034-1038
Author(s):  
P. Rama Rao
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cosine Similarity ◽  
Nearest Neighbours ◽  
And Performance ◽  
Content Based Filtering

Movies are one of the sources of entertainment, but the problem is in finding the content of our choice because content is increasing every year. However, recommendation systems plays here an important role for finding the content of desired domain in these situations. The aim of this paper is to improve the accuracy and performance of a filtration techniques existed. There are several methods and algorithms existed to implement a recommendation system. Content-based filtering is the simplest method, it takes input from the users, checks the movie and its content and recommends a list of similar movies. In this paper, to prove the effectiveness of our system, K-NN algorithms and collaborative filtering are used. Here, the usage of cosine similarity is done for recommending the nearest neighbours.

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.

scholarly journals Recommendation System for Purchasing Goods Based on the Decision Tree Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 121-127
Author(s):  
Yurii Kohut ◽  
◽  
Iryna Yurchak
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Preference Profile ◽  
Final Decision ◽  
Filtering Method ◽  
Binary Descriptors ◽  
Short Period ◽  
The Impact ◽  
Content Based Filtering

Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The content-based filtering method is based on the description of the object and the customer’s preference profile. An object description is a finite set of its descriptors, such as keywords, binary descriptors, etc., and a preference profile is a weighted vector of object descriptors in which scales reflect the importance of each descriptor to the client and its contribution to the final decision. This model selects items that are similar to the customer’s favorite items before. The second model, which implements the method of collaborative filtering, is based on information about the history of behavior of all customers on the resource: data on their purchases, assessments of product quality, reviews, marked product. The model finds clients that are similar in behavior and the recommendation is based on their assessments of this element. Voting was used to combine the results issued by individual models — the best result is chosen from the results of two models of the ensemble. This approach minimizes the impact of randomness and averages the errors of each model. The aim: The purpose of work is to create real competitive recommendation system for short period of time and minimum costs.

scholarly journals Collaborative Filtering: Data Sparsity Challenges

2018 ◽  
Vol 6 (6) ◽  
Author(s):  
Er.Meenakshi . ◽  
Dr.Satpal .
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Effective Means ◽  
Recommendation Systems ◽  
Decision Making Process ◽  
Huge Amount ◽  
Data Sparsity ◽  
Filtering Technique ◽  
The Cost

Today internet is a place where the huge amount of data is stored, there is need to sift, which create a problem for the internet user, so recommend system solve the problem. A recommendation system is a system that helps a user found the products and content by forecast the user’s rating of each item and showing them the items that they would rate highly. Recommendation systems are everywhere. With online shopping, customer has nearly infinite choices. No one has enough time to try every product for sale. Recommendation systems play an important role to solve the users search the products and content they care about. Recommendation system is a process of filtering the information that deal with information overloaded problems. Recommendation system is important for both user and service provider. It reduces the cost of transaction and selecting item in an online scenario it also improve the quality of decision making process. It is now an effective means for selling their product. So over emphasized of user is not good for recommendation system. To solve the problems of recommendation system like data sparsity we use one of best technique that is collaborative filtering technique.

scholarly journals Neural Collaborative with Sentence BERT for News Recommender System

2021 ◽  
Vol 5 (4) ◽  
pp. 448
Author(s):  
Budi Juarto ◽  
Abba Suganda Girsang
Keyword(s):  
Neural Networks ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Roc Curves ◽  
Leave One Out ◽  
Evaluation Measurement ◽  
Better Than ◽  
Roc Score

The number of news produced every day is as much as 3 million per day, making readers have many choices in choosing news according to each reader's topic and category preferences. The recommendation system can make it easier for users to choose the news to read. The method that can be used in providing recommendations from the same user is collaborative filtering. Neural collaborative filtering is usually being used for recommendation systems by combining collaborative filtering with neural networks. However, this method has the disadvantage of recommending the similarity of news content such as news titles and content to users. This research wants to develop neural collaborative filtering using sentences BERT. Sentence BERT is applied to news titles and news contents that are converted into sentence embedding. The results of this sentence embedding are used in neural collaboration with item id, user id, and news category. We use a Microsoft news dataset of 50,000 users and 51,282 news, with 5,475,542 interactions between users and news. The evaluation carried out in this study uses precision, recall, and ROC curves to predict news clicks by the user. Another evaluation uses a hit ratio with the leave one out method. The evaluation results obtained a precision value of 99.14%, recall of 92.48%, f1-score of 95.69%, and ROC score of 98%. Evaluation measurement using the hit ratio@10 produces a hit ratio of 74% at fiftieth epochs for neural collaborative with sentence BERT which is better than neural collaborative filtering (NCF) and NCF with news category.

A Recommendation System Framework Based on Web Mining

2012 ◽  
Vol 151 ◽  
pp. 576-582 ◽  
Author(s):  
Zhen Jian Yang ◽  
Ke Wen Xia
Keyword(s):  
Collaborative Filtering ◽  
Web Mining ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Web Content ◽  
Research Papers ◽  
Rating Data ◽  
Web Structure ◽  
Better Than

Presently recommendation systems have gradually become an important part in E-Commerce, more and more research papers about recommendation systems in E-Commerce appeared in many kinds of conferences and journals. With expanding of E-Commerce it also faces series of challenges. Traditional collaborative filtering recommendation technique is hard to provide recommendation service for unregistered users. To overcome this problem, we suggested a framework of recommendation system based on web mining. It is made up of two parts, offline and online. This method first clustered web usage data, web content data and web structure data respectively, then provided high-quality recommendation services based on mining results. Compared with traditional collaborative filtering techniques, recommendation systems based on web mining are convenient for users because user need not to provide user-rating data explicitly. In end of this paper, accuracy of recommendation system based on web mining was tested and compared with traditional collaborative filtering recommendation system. Testing results showed that, quality of recommendation system based on web mining is better than quality of traditional collaborative filtering recommendation system.

A feature-based hybrid recommender system for risk prediction : Machine learning approach (Preprint)

2020 ◽  
Author(s):  
Uzair Bhatti
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Correct Diagnosis ◽  
Recommendation Systems ◽  
Data Integrity ◽  
Machine Learning Algorithms ◽  
Patient Counseling ◽  
Hybrid Filtering ◽  
Novel Algorithm

BACKGROUND In the era of health informatics, exponential growth of information generated by health information systems and healthcare organizations demands expert and intelligent recommendation systems. It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting doctors, hospitals, medicine, diagnosis etc according to patients’ interests. OBJECTIVE Recommendation uses Hybrid Filtering as one of the most popular approaches, but the major limitations of this approach are selectivity and data integrity issues.Mostly existing recommendation systems & risk prediction algorithms focus on a single domain, on the other end cross-domain hybrid filtering is able to alleviate the degree of selectivity and data integrity problems to a better extent. METHODS We propose a novel algorithm for recommendation & predictive model using KNN algorithm with machine learning algorithms and artificial intelligence (AI). We find the factors that directly impact on diseases and propose an approach for predicting the correct diagnosis of different diseases. We have constructed a series of models with good reliability for predicting different surgery complications and identified several novel clinical associations. We proposed a novel algorithm pr-KNN to use KNN for prediction and recommendation of diseases RESULTS Beside that we compared the performance of our algorithm with other machine algorithms and found better performance of our algorithm, with predictive accuracy improving by +3.61%. CONCLUSIONS The potential to directly integrate these predictive tools into EHRs may enable personalized medicine and decision-making at the point of care for patient counseling and as a teaching tool. CLINICALTRIAL dataset for the trials of patient attached

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