Does a one-size recommendation system fit all? the effectiveness of collaborative filtering based recommendation systems across different domains and search modes

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.

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

Bayero Journal of Pure and Applied Sciences ◽

10.4314/bajopas.v13i2.6 ◽

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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Machine Learning as a method of adapting offers to the clients

Journal of Computer Sciences Institute ◽

10.35784/jcsi.1293 ◽

2019 ◽

Vol 13 ◽

pp. 267-271

Author(s):

Jacek Bielecki ◽

Oskar Ceglarski ◽

Maria Skublewska-Paszkowska

Keyword(s):

Machine Learning ◽

Collaborative Filtering ◽

Recommendation System ◽

Recommendation Systems ◽

Filtering Method ◽

Advantages And Disadvantages ◽

Information Filter ◽

User Ratings ◽

Research Show

Recommendation systems are class of information filter applications whose main goal is to provide personalized recommendations. The main goal of the research was to compare two ways of creating personalized recommendations. The recommendation system was built on the basis of a content-based cognitive filtering method and on the basis of a collaborative filtering method based on user ratings. The conclusions of the research show the advantages and disadvantages of both methods.

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Enhancing Item-Based Collaborative Filtering with Item Correlations for Music Recommendation System

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a2016.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 1548-1553

Keyword(s):

Collaborative Filtering ◽

Digital Libraries ◽

Correlation Matrix ◽

Recommendation System ◽

Pearson Correlation ◽

Recommendation Systems ◽

Vital Role ◽

Music Recommendation ◽

Filtering Method ◽

Music Recommendation System

Music recommendation systems are playing a vital role in suggesting music to the users from huge volumes of digital libraries available. Collaborative filtering (CF) is a one of the well known method used in recommendation systems. CF is either user centric or item centric. The former is known as user-based CF and later is known as item-based CF. This paper proposes an enhancement to item-based collaborative filtering method by considering correlation among items. Lift and Pearson Correlation coefficient are used to find the correlation among items. Song correlation matrix is constructed by using correlation measures. Proposed method is evaluated on the benchmark dataset and results obtained are compared with basic item-based CF

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Personalized Suggestion For Music Based On Collaborative Filtering

International Journal Of Engineering And Computer Science ◽

10.18535/ijecs/v9i05.4487 ◽

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.

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A Hybrid Ontology-Based Recommendation System in e-Commerce

Algorithms ◽

10.3390/a12110239 ◽

2019 ◽

Vol 12 (11) ◽

pp. 239 ◽

Cited By ~ 2

Author(s):

Márcio Guia ◽

Rodrigo Rocha Silva ◽

Jorge Bernardino

Keyword(s):

Collaborative Filtering ◽

Experimental Evaluation ◽

Recommendation System ◽

Knowledge Engineering ◽

Hybrid Approach ◽

Recommendation Systems ◽

The Internet ◽

Active User ◽

Expensive Process

The growth of the Internet has increased the amount of data and information available to any person at any time. Recommendation Systems help users find the items that meet their preferences, among the large number of items available. Techniques such as collaborative filtering and content-based recommenders have played an important role in the implementation of recommendation systems. In the last few years, other techniques, such as, ontology-based recommenders, have gained significance when reffering better active user recommendations; however, building an ontology-based recommender is an expensive process, which requires considerable skills in Knowledge Engineering. This paper presents a new hybrid approach that combines the simplicity of collaborative filtering with the efficiency of the ontology-based recommenders. The experimental evaluation demonstrates that the proposed approach presents higher quality recommendations when compared to collaborative filtering. The main improvement is verified on the results regarding the products, which, in spite of belonging to unknown categories to the users, still match their preferences and become recommended.

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Big Data Mining Using Collaborative Filtering

Web Services ◽

10.4018/978-1-5225-7501-6.ch038 ◽

2019 ◽

pp. 702-711

Author(s):

Anu Saini

Keyword(s):

Data Mining ◽

Big Data ◽

Collaborative Filtering ◽

Recommendation System ◽

Recommendation Systems ◽

Big Data Mining ◽

Detailed Survey ◽

Past Behavior

Today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behavior. Recommendation systems are used to provide the recommendation to the users. The author presents an overview of various types of recommendation systems and how these systems give recommendation by using various approaches of Collaborative Filtering. Various research works that employ collaborative filtering for recommendations systems are reviewed and classified by the authors. Finally, this chapter focuses on the framework of recommendation system of big data along with the detailed survey on the use of the Big Data mining in collaborative filtering.

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A review of a recommendation filtering system approach based on reliable sustainable opinion mining

Technological Sustainability ◽

10.1108/techs-09-2021-0012 ◽

2022 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Imen Gmach ◽

Nadia Abaoub ◽

Rubina Khan ◽

Naoufel Mahfoudh ◽

Amira Kaddour

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Information Search ◽

Recommendation System ◽

Opinion Mining ◽

Information Filtering ◽

Recommendation Systems ◽

Content Filtering ◽

Content Type ◽

The Impact

PurposeIn this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.Design/methodology/approachMethodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.FindingsThe purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.Originality/valueThe authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

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Collaborative Filtering-Based Recommendation System Using Time Decay Model

International Journal of e-Collaboration ◽

10.4018/ijec.2021070106 ◽

2021 ◽

Vol 17 (3) ◽

pp. 85-100

Author(s):

Jayaraman Parthasarathy ◽

Ramesh Babu Kalivaradhan

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Recommendation Systems ◽

User Engagement ◽

Confidence Bound ◽

Time Decay ◽

Specific Data ◽

Movie Recommendation ◽

Upper Confidence Bound ◽

F Measure

Online collaborative movie recommendation systems attempt to help customers accessing their favourable movies by gathering exactly comparable neighbors between the movies from their chronological identical ratings. Collaborative filtering-based movie recommendation systems require viewer-specific data, and the need for collecting viewer-specific data diminishes the effectiveness of the recommendation. To solve this problem, the authors employ an effective multi-armed bandit called upper confidence bound, which is applied to automatically recommend the movies for the users. In addition, the concept of time decay is provided in a mathematical definition that redefines the dynamic item-to-item similarity. Then, two patterns of time decay are analyzed, namely concave and convex functions, for simulation. The experiment test the MovieLens 100K dataset. The proposed method attains a maximum F-measure of 98.45 whereas the existing method reaches a minimum F-measure of only 95.60. The presented model adaptively responds to new users, can provide a better service, and generate more user engagement.

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Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

Symmetry ◽

10.3390/sym12091566 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1566 ◽

Cited By ~ 2

Author(s):

Zeinab Shahbazi ◽

Debapriya Hazra ◽

Sejoon Park ◽

Yung Cheol Byun

Keyword(s):

Machine Learning ◽

South Korea ◽

Collaborative Filtering ◽

Mean Square Error ◽

Prediction Accuracy ◽

Recommendation System ◽

Recommendation Systems ◽

Gradient Boosting ◽

Mean Square ◽

Extreme Gradient Boosting

With the spread of COVID-19, the “untact” culture in South Korea is expanding and customers are increasingly seeking for online services. A recommendation system serves as a decision-making indicator that helps users by suggesting items to be purchased in the future by exploring the symmetry between multiple user activity characteristics. A plethora of approaches are employed by the scientific community to design recommendation systems, including collaborative filtering, stereotyping, and content-based filtering, etc. The current paradigm of recommendation systems favors collaborative filtering due to its significant potential to closely capture the interest of a user as compared to other approaches. The collaborative filtering harnesses features like user-profile details, visited pages, and click information to determine the interest of a user, thereby recommending the items that are related to the user’s interest. The existing collaborative filtering approaches exploit implicit and explicit features and report either good classification or prediction outcome. These systems fail to exhibit good results for both measures at the same time. We believe that avoiding the recommendation of those items that have already been purchased could contribute to overcoming the said issue. In this study, we present a collaborative filtering-based algorithm to tackle big data of user with symmetric purchasing order and repetitive purchased products. The proposed algorithm relies on combining extreme gradient boosting machine learning architecture with word2vec mechanism to explore the purchased products based on the click patterns of users. Our algorithm improves the accuracy of predicting the relevant products to be recommended to the customers that are likely to be bought. The results are evaluated on the dataset that contains click-based features of users from an online shopping mall in Jeju Island, South Korea. We have evaluated Mean Absolute Error, Mean Square Error, and Root Mean Square Error for our proposed methodology and also other machine learning algorithms. Our proposed model generated the least error rate and enhanced the prediction accuracy of the recommendation system compared to other traditional approaches.

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