MuSIF: A Product Recommendation System Based on Multi-source Implicit Feedback

Schoinas, Ιοannis; Tjortjis, Christos

doi:10.1007/978-3-030-19823-7_55

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…The different models of recommendation system have been proposed over the last few years to analyze and forecast various business domains from different perspectives. Ioannis et al in their paper [4] present a novel a product recommendation system that is based on the concept of Implicit Feedback. They named it MuSIF.…”

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.

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“…An intelligent system is a special type of recommender system used to exploit the historical user ratings on data that comes from mined-relevant data through data mining process [5]. Therefore, to achieve the same proposed framework, methodologies like content-based (CB) [6], collaborative based (CF) [7], and hybrid filtering (HF) [8] techniques are required. Furthermore, a lot of factors in learner qualities make a distinction based upon information, learning style and configurations of consecutive learning where methodologies like CF, CB individually are not sufficient or reasonable to detect the distinction [9].…”

Section: A Recommender System Extracts User's Interestmentioning

confidence: 99%

“…Hence, the selected neighbourhood by HAR-KNN is similar to achieving the target products by the traditional methods. Moreover, the accuracy of the recall, F1 measure, and precision are computed to rate the relevant products and number of recommendations that were set to [2,4,6,8,10,12]. Therefore, the HAR-KNN provides better RS with better predictive accuracy than existing methods for the Amazon dataset.…”

Section: Case Studymentioning

confidence: 99%

A Hybrid Action-Related K-Nearest Neighbour (HAR-KNN) Approach for Recommendation Systems

et al. 2020

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Recommendation System (RS) has been broadly utilized in various areas and discovers product recommendations during an active user interaction in E-Commerce sites. Tremendous growth of users and products in recent years has faced some key challenges. There are numerous online sites that present many decisions to the user at once, which is strenuous. Moreover, finding active user or right product is an important task in RS. Existing works have been proposed to recommend a product by considering user inclination and socio-demographic behaviour. In this paper, we propose a Hybrid Action-Related K-Nearest Neighbour similarity (HAR-KNN) recommender that consolidates the simplicity of hybrid filtering to enrich user behaviour matrix with formation of the vector of features. It will classify the features using race classifiers from both quality and quantity aspects. The proposed approach also addresses the problems of the previous methods to efficiently evaluate user preference on products and balance feature analysis. The K-NN classification method has been qualified online and real-time to find user behaviour data coordinating to a specific user group containing the relationship between the similarity of many users and target users from a huge amount of data. The proposed experimental result is evaluated based on measures such as Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Squared Error (RMSE) with the lowest error of 0.7165, 0.7201 and 0.7322 separately. High predictive measures like Precision (P), Recall (R) and F1 are found to have values 0.8501, 0.2201 and 0.3507 respectively. INDEX TERMS Recommendation system (RS), user behaviour data, hybrid filtering, K-NN, behavioural matrix.

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“…The first one was created by scraping a real e-commerce site and comprises information for thousands of products. The second one was created by preprocessing an existing dataset that has been used for similar purposes in the past [27]. These datasets are described next.…”

Section: Datamentioning

confidence: 99%

“…Recommended items can potentially be anything that a user is looking for, such as products, movies, songs, services etc. [25][26][27]. E-commerce is a domain that RS are used extensively [1,2].…”

Section: Introductionmentioning

confidence: 99%

Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH

Beleveslis

Tjortjis

2020

IFIP Advances in Information and Communication Technology

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This work proposes an efficient Content-Based (CB) product recommendation methodology that promotes diversity. A heuristic CB approach incorporating feature weighting and Locality-Sensitive Hashing (LSH) is used, along with the TF-IDF method and functionality of tuning the importance of product features to adjust its logic to the needs of various e-commerce sites. The problem of efficiently producing recommendations, without compromising similarity, is addressed by approximating product similarities via the LSH technique. The methodology is evaluated on two sets with real e-commerce data. The evaluation of the proposed methodology shows that the produced recommendations can help customers to continue browsing a site by providing them with the necessary "next step". Finally, it is demonstrated that the methodology incorporates recommendation diversity which can be adjusted by tuning the appropriate feature weights.

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MuSIF: A Product Recommendation System Based on Multi-source Implicit Feedback

Cited by 10 publications

References 19 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 Hybrid Action-Related K-Nearest Neighbour (HAR-KNN) Approach for Recommendation Systems

Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH

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