User Recommender System Based on Knowledge, Availability, and Reputation From Interactions in Forums

Aciar, Silvana; Aciar, Gabriela; Collázos, César A.; González, Carina Soledad González

doi:10.1109/rita.2016.2518441

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…Experimental results have shown that MCRSs outperform state of the art recommender systems. Aciar et al [8] in their paper, design a recommender system that considers the degree of knowledge of the user, and the user's availability into account before making recommendations. This paper calculates the reputation of the users within their community, and uses this reputation to help design the recommendation systems.…”

Section: Related Workmentioning

confidence: 99%

Recommendation of Influenced Products Using Association Rule Mining: Neo4j as a Case Study

Sen

Mehta²,

Ganguli³

et al. 2021

SN COMPUT. SCI.

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Recommendation systems are now inherent for many business applications to take important business decisions. These systems are built based on the historical data that may be the sales data or customer feedback etc. Customer feedback is very important for any organization as it reflects the view, sentiment of the customers. Online systems allow customers to purchase products at a glance from any e-commerce website. Generally, the potential buyers check the review of the products to take informed decision of purchase. In this work, we attempt to build a recommendation model to find out the influence of a product on another product so that if a user purchases the influential product then the recommender system can recommend the influenced products to the users. In this paper, the recommendation system has been built based on association rule mining. We proposed a new association rule mining technique for quick decision-making and it gives better performance over Apriori algorithm which is one of the most popular approaches for association rule mining. The entire framework has been developed in Neo4j graph data model for doing the data modelling from raw text file and also to perform the analysis. We used real-life customer feedback data of amazon for experimental purpose. Keywords Recommendation system • Apriori algorithm • Association rule mining • Neo4j • Influential productThis article is part of the topical collection "Applications of Software Engineering and Tool Support" guest edited by Nabendu Chaki, Agostino Cortesi and Anirban Sarkar.

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Section: Related Workmentioning

confidence: 99%

Recommendation of Influenced Products Using Association Rule Mining: Neo4j as a Case Study

Sen

Mehta²,

Ganguli³

et al. 2021

SN COMPUT. SCI.

View full text Add to dashboard Cite

show abstract

“…This technique provides more personalized recommendations because much detailed information about the user is involved in determining the recommendations. Apart from this, there are knowledge-based recommendation systems that make use of possible user-item knowledge displaying how a peculiar item meets user demand to develop or predict the recommendations [9].…”

Section: Related Workmentioning

confidence: 99%

“…8.3 If there are few repetitions in the number of instances and max appears various times in Ci, then eliminate the entries with lowest rating, for that particular instance and repeat the step8. 9. Provide a relevant movie recommendation to the group of users (G).…”

Section: Numbers Of Users Their Weightage and Instances Withmentioning

confidence: 99%

A Novel Rule based Data Mining Approach towards Movie Recommender System

Sharma

Ahuja

Kumar

2020

JIOS

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The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.

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“…Finally, we conclude and show the directions for future work in Section 5. Aciar, Aciar, Collazos, and Gonzalez (2016) presented an innovative user recommender system that harnessed knowledge, availability, and reputation derived from interactions within online forums. This study showcased the potential of incorporating user-generated content and social interactions to enhance recommendation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Dynamic matrix factorization-based collaborative filtering in movie recommendation services

Nguyen,

Vo,

Nguyen

2024

HCMCOUJS - ENGINEERING AND TECHNOLOGY

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Movies are a primary source of entertainment, but finding specific content can be challenging given the exponentially increasing number of movies produced each year. Recommendation systems are extremely useful for solving this problem. While various approaches exist, Collaborative Filtering (CF) is the most straightforward. CF leverages user input and historical preferences to determine user similarity and suggest movies. Matrix Factorization (MF) is one of the most popular Collaborative Filtering (CF) techniques. It maps users and items into a joint latent space, using a vector of latent features to represent each user or item. However, traditional MF techniques are static, while user cognition and product variety are constantly evolving. As a result, traditional MF approaches struggle to accommodate the dynamic nature of user-item interactions. To address this challenge, we propose a Dynamic Matrix Factorization CF model for movie recommendation systems (DMF-CF) that considers the dynamic changes in user interactions. To validate our approach, we conducted evaluations using the standard MovieLens dataset and compared it to state-of-the-art models. Our preliminary findings highlight the substantial benefits of DMF-CF, which outperforms recent models on the MovieLens-100K and MovieLens-1M datasets in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics.

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User Recommender System Based on Knowledge, Availability, and Reputation From Interactions in Forums

Cited by 12 publications

References 8 publications

Recommendation of Influenced Products Using Association Rule Mining: Neo4j as a Case Study

Recommendation of Influenced Products Using Association Rule Mining: Neo4j as a Case Study

A Novel Rule based Data Mining Approach towards Movie Recommender System

Dynamic matrix factorization-based collaborative filtering in movie recommendation services

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