A Fast Recommender System for Cold User Using Categorized Items

Jazayeriy, Hamid; Mohammadi, Saghi; Shamshirband, Shahaboddin

doi:10.3390/mca23010001

Cited by 20 publications

(7 citation statements)

References 31 publications

(53 reference statements)

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“…Moreover, we evaluate the performance of the proposed and baseline methods for more difficult situations having cold-start users whose number of rated items is less than 20, where item-based CF and spectral clustering are used. Due to the fact that the MovieLens 100K dataset does not contain records for cold-start users, we modify the experimental setup according to [42]. Specifically, we first select users who have rated between 20-30 items as the testing set, consisting of 290 users, and make the number of rated items of each selected user in the range between 3 and 20 via random masking.…”

Section: Resultsmentioning

confidence: 99%

Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model

et al. 2019

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Giving or recommending appropriate content based on the quality of experience is the most important and challenging issue in recommender systems. As collaborative filtering (CF) is one of the most prominent and popular techniques used for recommender systems, we propose a new clusteringbased CF (CBCF) method using an incentivized/penalized user (IPU) model only with the ratings given by users, which is thus easy to implement. We aim to design such a simple clustering-based approach with no further prior information while improving the recommendation accuracy. To be precise, the purpose of CBCF with the IPU model is to improve recommendation performance such as precision, recall, and F 1 score by carefully exploiting different preferences among users. Specifically, we formulate a constrained optimization problem in which we aim to maximize the recall (or equivalently F 1 score) for a given precision. To this end, users are divided into several clusters based on the actual rating data and Pearson correlation coefficient. Afterward, we give each item an incentive/penalty according to the preference tendency by users within the same cluster. Our experimental results show a significant performance improvement over the baseline CF scheme without clustering in terms of recall or F 1 score for a given precision. INDEX TERMS Clustering, collaborative filtering, F 1 score, incentivized/penalized user model, Pearson correlation coefficient, recommender system.

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Section: Resultsmentioning

confidence: 99%

Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model

et al. 2019

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“…[21] developed a dynamic analysis classification technique by implementing ML and evaluated the different variables in these learning approaches. A public repository response assessment technique [22] discussed huge data volumes on Twitter to create the emotional state of every message. [23] describes the user opinion mining system used to extract similar users' views from the person's view using a moderate data analysis method.…”

Section: Literature Surveymentioning

confidence: 99%

Knowledge Extraction from Open Data Repository

Kakulapati¹

2022

Open Data

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The explosion of affluent social networks, online communities, and jointly generated information resources has accelerated the convergence of technological and social networks producing environments that reveal both the framework of the underlying information arrangements and the collective formation of their members. In studying the consequences of these developments, we face the opportunity to analyze the POD repository at unprecedented scale levels and extract useful information from query log data. This chapter aim is to improve the performance of a POD repository from a different point of view. Firstly, we propose a novel query recommender system to help users shorten their query sessions. The idea is to find shortcuts to speed up the user interaction with the open data repository and decrease the number of queries submitted. The proposed model, based on pseudo-relevance feedback, formalizes exploiting the knowledge mined from query logs to help users rapidly satisfy their information need.

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“…To make the System more accurate, Jannach et al [26] presented regression-based and item-based recommendation. The developed recommendation systems with different researchers used collaborative filtering techniques and algorithms [27][28][29][30][31][32][33]. Collaborative filtering gets information based on user input knowledge and evaluates the relationship between different users to accomplish specific deductions of feature spaces.…”

Section: Recommendation Systemmentioning

confidence: 99%

Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners

Shahbazi

Byun

2020

Symmetry

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Electronic Learning (e-learning) has made a great success and recently been estimated as a billion-dollar industry. The users of e-learning acquire knowledge of diversified content available in an application using innovative means. There is much e-learning software available—for example, LMS (Learning Management System) and Moodle. The functionalities of this software were reviewed and we recognized that learners have particular problems in getting relevant recommendations. For example, there might be essential discussions about a particular topic on social networks, such as Twitter, but that discussion is not linked up and recommended to the learners for getting the latest updates on technology-updated news related to their learning context. This has been set as the focus of the current project based on symmetry between user project specification. The developed project recommends relevant symmetric articles to e-learners from the social network of Twitter and the academic platform of DBLP. For recommendations, a Reinforcement learning model with optimization is employed, which utilizes the learners’ local context, learners’ profile available in the e-learning system, and the learners’ historical views. The recommendations by the system are relevant tweets, popular relevant Twitter users, and research papers from DBLP. For matching the local context, profile, and history with the tweet text, we recognized that terms in the e-learning system need to be expanded to cover a wide range of concepts. However, this diversification should not include such terms which are irrelevant. To expand terms of the local context, profile and history, the software used the dataset of Grow-bag, which builds concept graphs of large-scale Computer Science topics based on the co-occurrence scores of Computer Science terms. This application demonstrated the need and success of e-learning software that is linked with social media and sends recommendations for the content being learned by the e-Learners in the e-learning environment. However, the current application only focuses on the Computer Science domain. There is a need for generalizing such applications to other domains in the future.

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A Fast Recommender System for Cold User Using Categorized Items

Cited by 20 publications

References 31 publications

Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model

Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model

Knowledge Extraction from Open Data Repository

Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners

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