Scalable and adaptive collaborative filtering by mining frequent item co-occurrences in a user feedback stream

Yağcı, A. Murat; Aytekin, Tevfik; Gürgen, Fikret

doi:10.1016/j.engappai.2016.10.011

Cited by 22 publications

(6 citation statements)

References 36 publications

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“…The source of the traditional interest list is the formula of the target user's neighbor user set [22]. In this study, two kinds of interest list generation methods were used for comparative analysis, as shown in Figures 5 and 6, which are respectively an interest list of determined number of and a list of interest recommendations generated by the threshold filtering method.…”

Section: The Generation Methods Of Interest Listsmentioning

confidence: 99%

Application of Collaborative Filtering Algorithm in Mathematical Expressions of User Personalized Information Recommendation

Qian¹

2019

IJCIS

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In order to solve the shortcomings of the existing mathematical expressions of the user's personalized recommendation search and to increase the accuracy of the user's personalized information recommendation, in this study, the collaborative filtering algorithm, the related theory of fuzzy sets, and the evaluation methods were introduced at first. Then, the establishment of collaborative filtering model and working principle, user similarity measure, generation of mathematical expression recommendation list, design of mathematical expression recommendation model and implementation of function were introduced. Finally, the application of the collaborative filtering algorithm in the user's personalized information recommendation mathematical expression was verified by collecting data. The results showed that the recommendation mathematical expression model based on collaborative filtering algorithm had higher accuracy in collecting user personalized information, and the accuracy of the system was higher than other recommendation algorithms, which can better meet the individual needs of users. This research can provide a fast and convenient way for users to search for personalized information, and has a good guiding significance for the retrieval and recommendation of informational scientific and technical literature.

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Section: The Generation Methods Of Interest Listsmentioning

confidence: 99%

Application of Collaborative Filtering Algorithm in Mathematical Expressions of User Personalized Information Recommendation

Qian¹

2019

IJCIS

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“…Prequential evaluation has been used in [8], [24], [25], [26], [27] in stream-based recommendation problems, but without statistical testing. In [7] the focus is on the actual prequential evaluation methodology, and proposes statistical testing, using McNemar's test over a sliding window with arbitrary size.…”

Section: Related Workmentioning

confidence: 99%

Statistically Robust Evaluation of Stream-Based Recommender Systems

Vinagre

Jorge

Rocha

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Online incremental models for recommendation are nowadays pervasive in both the industry and the academia. However, there is not yet a standard evaluation methodology for the algorithms that maintain such models. Moreover, online evaluation methodologies available in the literature generally fall short on the statistical validation of results, since this validation is not trivially applicable to stream-based algorithms. We propose a k-fold validation framework for the pairwise comparison of recommendation algorithms that learn from user feedback streams, using prequential evaluation. Our proposal enables continuous statistical testing on adaptive-size sliding windows over the outcome of the prequential process, allowing practitioners and researchers to make decisions in real time based on solid statistical evidence. We present a set of experiments to gain insights on the sensitivity and robustness of two statistical tests -McNemar's and Wilcoxon signed rank -in a streaming data environment. Our results show that besides allowing a real-time, fine-grained online assessment, the online versions of the statistical tests are at least as robust as the batch versions, and definitely more robust than a simple prequential single-fold approach.

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“…For example, in [7] Guo et al proposed a novel method called "Merge" to incorporate social trust information, and supplement user preference by merging users' trusted neighbor ratings. In [8] Hu et al integrated time information into collaborative filtering similarity measure in collaborative filtering algorithm, and designed a hybrid personalized random walk algorithm; Yong-ping Du et al [5] proposed item-based RBM, and used deep and multilayer RBM network structure to solve the problem of data sparsity; Sedhain et al [20] generalized matrix algebra framework, and they doesn't need the target user's data when the side information is available ; Jian Wei et al [25] put forward two models on the basis of a framework based on tight-coupling collaborative filtering and the in-depth study into neural network; A. Murat Yagci et al [26] focused on frequent co-occurrence items and proposed SASCF to eliminate the cold start of the system; Su Hongyi et al [22] proposed a new algorithm involving time decay factor in the CF algorithm, and deployed time weights on the MapReduce parallel computing framework ; Xiuju Liu et al [13] presented a new algorithm of CF-ISEGB, and took the influence sets of current e-learning groups into consideration to effectively solve problems caused by sparse data sets.…”

Section: Application Of Improved Collaborative Filtering In the Recom...mentioning

confidence: 99%

Application of Improved Collaborative Filtering in the Recommendation of E-commerce Commodities

Chang

Gui

Fan

et al. 2019

INT J COMPUT COMMUN

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Problems such as low recommendation precision and efficiency often exist in traditional collaborative filtering because of the huge basic data volume. In order to solve these problems, we proposed a new algorithm which combines collaborative filtering and support vector machine (SVM). Different with traditional collaborative filtering, we used SVM to classify commodities into positive and negative feedbacks. Then we selected the commodities that have positive feedback to calculate the comprehensive grades of marks and comments. After that, we build SVM-based collaborative filtering algorithm. Experiments on Taobao data (a Chinese online shopping website owned by Alibaba) showed that the algorithm has good recommendation precision and recommendation efficiency, thus having certain practical value in the E-commerce industry.

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Scalable and adaptive collaborative filtering by mining frequent item co-occurrences in a user feedback stream

Cited by 22 publications

References 36 publications

Application of Collaborative Filtering Algorithm in Mathematical Expressions of User Personalized Information Recommendation

Application of Collaborative Filtering Algorithm in Mathematical Expressions of User Personalized Information Recommendation

Statistically Robust Evaluation of Stream-Based Recommender Systems

Application of Improved Collaborative Filtering in the Recommendation of E-commerce Commodities

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