RecSys Pertaining to Research Information with Collaborative Filtering Methods: Characteristics and Challenges

Azeroual, Otmane; Koltay, Tibor

doi:10.3390/publications10020017

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…Despite the long history of the neighborhood based CF methods, they are still widely used in both research and industry (such as in research management areas [22] and Amazon. com (accessed at 22 April 2022) [23]) because of its advantages of easy implementation, good interpretability and intuitive simplicity.…”

Section: Related Workmentioning

confidence: 99%

Weighted Similarity and Core-User-Core-Item Based Recommendations

Zhang

Dong

2022

Entropy

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In traditional recommendation algorithms, the users and/or the items with the same rating scores are equally treated. In real world, however, a user may prefer some items to other items and some users are more loyal to a certain item than other users. In this paper, therefore, we propose a weighted similarity measure by exploiting the difference in user-item relationships. In particular, we refer to the most important item of a user as his core item and the most important user of an item as its core user. We also propose a Core-User-Item Solver (CUIS) to calculate the core users and core items of the system, as well as the weighting coefficients for each user and each item. We prove that the CUIS algorithm converges to the optimal solution efficiently. Based on the weighted similarity measure and the obtained results by CUIS, we also propose three effective recommenders. Through experiments based on real-world data sets, we show that the proposed recommenders outperform corresponding traditional-similarity based recommenders, verify that the proposed weighted similarity can improve the accuracy of the similarity, and then improve the recommendation performance.

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Section: Related Workmentioning

confidence: 99%

Weighted Similarity and Core-User-Core-Item Based Recommendations

Zhang

Dong

2022

Entropy

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“…The basic assumptions of the collaborative filtering methods are that a particular user group is likely to use similar items in the future [24,25]. Therefore, it elaborates on making well-classified user groups based on complex criteria.…”

Section: Introductionmentioning

confidence: 99%

Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students

Kim,

Lim

2023

Applied Sciences

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Various services and applications based on information and communications technology (ICT) are converging with cultural aspects of historical implementations. At the same time, developing a convergence course for non-ICT majors is becoming increasingly popular in universities. In this paper, we develop an AI application course for non-ICT major university students toward convergence with recommendation systems and Silk Road studies. Based on our five-year research on the martial arts, dance, and play of seven Silk Road countries, we have created and categorized an accessible database for 177 items in those countries. For our convergence course, we measure the similarity between the items for summary and perform collaborative filtering based on alternating least squares (ALS) matrix factorization so that our prototyped intelligent recommendation engine can predict the items in which a user might be interested. The course is designed to teach non-ICT major university students not only historical aspects of the Silk Road but also implementation aspects of recommendation systems with web services.

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“…On par with search engines, recommender systems are able to relieve this problem by modeling user preferences based on collected historical data [ 1 ]. According to the used data, recommendation models can be mainly categorized into collaborative filtering models [ 2 , 3 ], content-enriched models [ 4 , 5 ], and context-enriched models [ 6 , 7 ]. Collaborative filtering models render recommendations based on the similarity of users or items from the user–item interactions history.…”

Section: Introductionmentioning

confidence: 99%

Time-Aware Explainable Recommendation via Updating Enabled Online Prediction

Jiang

Zeng

2022

Entropy

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There has been growing attention on explainable recommendation that is able to provide high-quality results as well as intuitive explanations. However, most existing studies use offline prediction strategies where recommender systems are trained once while used forever, which ignores the dynamic and evolving nature of user–item interactions. There are two main issues with these methods. First, their random dataset split setting will result in data leakage that knowledge should not be known at the time of training is utilized. Second, the dynamic characteristics of user preferences are overlooked, resulting in a model aging issue where the model’s performance degrades along with time. In this paper, we propose an updating enabled online prediction framework for the time-aware explainable recommendation. Specifically, we propose an online prediction scheme to eliminate the data leakage issue and two novel updating strategies to relieve the model aging issue. Moreover, we conduct extensive experiments on four real-world datasets to evaluate the effectiveness of our proposed methods. Compared with the state-of-the-art, our time-aware approach achieves higher accuracy results and more convincing explanations for the entire lifetime of recommendation systems, i.e., both the initial period and the long-term usage.

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RecSys Pertaining to Research Information with Collaborative Filtering Methods: Characteristics and Challenges

Cited by 5 publications

References 35 publications

Weighted Similarity and Core-User-Core-Item Based Recommendations

Weighted Similarity and Core-User-Core-Item Based Recommendations

Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students

Time-Aware Explainable Recommendation via Updating Enabled Online Prediction

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