Fast algorithms to evaluate collaborative filtering recommender systems

Zhang, Feng; Gong, Ti; Lee, Victor E.; Zhao, Gansen; Rong, Chunming; Qu, Guangzhi

doi:10.1016/j.knosys.2015.12.025

Cited by 77 publications

(41 citation statements)

References 16 publications

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“…The state of the work has been implemented on video recommendation domain. The focus on improving the e±ciency of recommendation algorithm in the context of big data has been carried out by Zhang et al (2016). They have conducted experiments for the reduction of computation time and evaluated in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics which are most representative and e®ective ones.…”

Section: Research Attempts Based On Local Pattern Analytics Strategymentioning

confidence: 99%

A Big Data Recommendation Engine Framework Based on Local Pattern Analytics Strategy for Mining Multi-Sourced Big Data

Venkatesan

Saravanan

Ramkumar

2019

J. Info. Know. Mgmt.

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Organisations that perform business operations in a multi-sourced big data environment are in imperative need to discover meaningful patterns of interest from their diversified data sources. With the advent of big data technologies such as Hadoop and Spark, commodity hardwares play vital role in the task of data analytics and process the multi-sourced and multi-formatted big data in a reasonable cost and time. Though various data analytic techniques exist in the context of big data, recommendation system is more popular in web-based business applications to suggest suitable products, services, and items to potential customers. In this paper, we put forth a big data recommendation engine framework based on local pattern analytics strategy to explore user preferences and taste for both branch level and central level decisions. The framework encourages the practice of moving computing environment towards the data source location and avoids forceful integration of data. Further it assists decision makers to reap hidden preferences and taste of users from branch data sources for an effective customer campaign. The novelty of the framework has been evaluated in the benchmark dataset, MovieLens100k and results clearly confirm the advantages of the proposal.

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Section: Research Attempts Based On Local Pattern Analytics Strategymentioning

confidence: 99%

A Big Data Recommendation Engine Framework Based on Local Pattern Analytics Strategy for Mining Multi-Sourced Big Data

Venkatesan

Saravanan

Ramkumar

2019

J. Info. Know. Mgmt.

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“…To test the accuracy of the model, we use the evaluation method based on model recommendations [7,8]. This method evaluates the accuracy of the model by comparing the model's recommendations with the choice of users purchase by using three metrics: P recision, Recall, and F measure to measure the accuracy of the model [11,19].…”

Section: Evaluate the Accuracy Of The Modelmentioning

confidence: 99%

Collaborative Recommenderation Based on Statistical Implication Rules

Phan

Dang

Huynh³

2018

JCC

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Abstract. In recent researches, many approaches based on association rules have been proposed to improve the accuracy of recommender systems. These approaches are primarily based on Apriori data mining algorithm in order to generate the association rules and apply them to improving the recommendation results. However, these approaches also reveal some disadvantages of the system, such as taking a longer time for generating association rules; applying the Apriori algorithm on rating sparse matrix resulting in irrelevant information and causing poor recommendation results to target users and association rules generated primarily relying on given threshold of Support and Confidence measures leading to the focus on the majority of rules and ignoring the astonishment of rules to affect the recommendation results. In this study, we propose a new model for collaborative filtering recommender systems: The collaborative recommendation is based on statistical implication rules (IIR); Differently from collaborative recommendation based on association rules (AR), the IIR predicts the items for users based on statistical implication rules generated from rating matrix and Implication intensity measures measuring the surprisingness of rules. To evaluate the effectiveness of the model, in the experimental section, we implement the model on three real datasets and compare the results with some different effective models. The results show that the IIR has higher precision on the experimental datasets.

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“…Đánh giá độ chính xác của mô hình tư vấn là một khâu quan trọng trong quy trình xây dựng hệ tư vấn [5][4] [3]. Nó giúp cho người thiết kế mô hình lựa chọn mô hình, kiểm tra độ chính xác của mô hình trước khi đưa mô hình vào ứng dụng thực tế.…”

Section: đáNh Giá Mô Hình Tƣ Vấnunclassified