Healthcare Providers Recommender System Based on Collaborative Filtering Techniques

Background: A recommender system captures the user preferences and behaviour to provide a relevant recommendation to the user. In a hybrid model-based recommender system, it requires a pre-trained data model to generate recommendations for a user. Ontology helps to represent the semantic information and relationships to model the expressivity and linkage among the data. Methods: We enhanced the matrix factorization model accuracy by utilizing ontology to enrich the information of the user-item matrix by integrating the item-based and user-based collaborative filtering techniques. In particular, the combination of enriched data, which consists of semantic similarity together with rating pattern, will help to reduce the cold start problem in the model-based recommender system. When the new user or item first coming into the system, we have the user demographic or item profile that linked to our ontology. Thus, semantic similarity can be calculated during the item-based and user-based collaborating filtering process. The item-based and user-based filtering process are used to predict the unknown rating of the original matrix. Results: Experimental evaluations have been carried out on the MovieLens 100k dataset to demonstrate the accuracy rate of our proposed approach as compared to the baseline method using (i) Singular Value Decomposition (SVD) and (ii) combination of item-based collaborative filtering technique with SVD. Experimental results demonstrated that our proposed method has reduced the data sparsity from 0.9542% to 0.8435%. In addition, it also indicated that our proposed method has achieved better accuracy with Root Mean Square Error (RMSE) of 0.9298, as compared to the baseline method (RMSE: 0.9642) and the existing method (RMSE: 0.9492). Conclusions: Our proposed method enhanced the dataset information by integrating user-based and item-based collaborative filtering techniques. The experiment results shows that our system has reduced the data sparsity and has better accuracy as compared to baseline method and existing method.

Download Full-text

Exploration of Collaboration Filtering Techniques for Product Recommendation

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c5348.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 799-802

Keyword(s):

Collaborative Filtering ◽

Search Engine ◽

Recommender System ◽

Recommendation System ◽

Scientific Research ◽

Knowledge Extraction ◽

User Preferences ◽

Education Economics ◽

Product Recommendation ◽

Filtering Techniques

Today, recommendation system has been globally adopted as the most effective and reliable search engine for knowledge extraction in the field of education, economics and scientific research. Collaborative filtering is a proven techniques used in recommender system to make predictions or recommendations of the unknown preferences for users based on the known user preferences. In this paper, collaborative filtering task and their challenges are explored, study the different recommendation techniques and evaluate their performance using different metrics.

Download Full-text

Accuracy Invoked Course Recommender System using Collaborative Filtering

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9981.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 9691-9695

Keyword(s):

Content Analysis ◽

Collaborative Filtering ◽

Recommender System ◽

Mental Ability ◽

Student Selection ◽

Field Of Study ◽

The Right ◽

Content Based Filtering ◽

Ranked List ◽

Filtering Techniques

It is always important that the student should choose the right course. The decision of taking the right course is very important as the student’s future depends on the course they opt to study. Most of the students are not much aware of the courses that are available in their own field of study. Selecting wrong courses might be due to the mismatch between the student’s aptitude, training and mental ability. Thus, the idea is to develop a system for helping the students to choose a course which would be best suited for him/her based on features like previous student selection, interest, languages known etc. Existing research has explored recommender system using content-based filtering but it can only do limited content analysis and the recommendation will not be precise at the end. An attempt is made to improve the performance of this system using Collaborative based filtering techniques which will recommend a ranked list of courses. Under Collaborative filtering techniques, user based collaborative filtering and item based collaborative filtering is used. Sample student datasets from Kaggle, has been used to test the performance of our system.

Download Full-text

Building Error-Reflected Models for Collaborative Filtering Recommender System

The KIPS Transactions PartD ◽

10.3745/kipstd.2009.16-d.3.451 ◽

2009 ◽

Vol 16D (3) ◽

pp. 451-462

Author(s):

Heung-Nam Kim ◽

Geun-Sik Jo

Keyword(s):

Collaborative Filtering ◽

Recommender System

Download Full-text

Identifying Attack Models for Securing Cluster-Based Recommendation System

Recent Patents on Engineering ◽

10.2174/1872212114666200403091053 ◽

2020 ◽

Vol 14 ◽

Author(s):

Amreen Ahmad ◽

Tanvir Ahmad ◽

Ishita Tripathi

Keyword(s):

Collaborative Filtering ◽

Recommender System ◽

Recommendation System ◽

Relevant Information ◽

System Function ◽

Recommendation Algorithm ◽

Underlying Network ◽

Entire Dataset ◽

Attack Models ◽

Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

Download Full-text