User profiling approaches for demographic recommender systems

Al-Shamri, Mohammad Yahya H.

doi:10.1016/j.knosys.2016.03.006

Cited by 130 publications

(46 citation statements)

References 18 publications

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“…Keeping in mind the sparsity and accuracy issues while dealing with bulk amount of items in a dataset for an e-commerce site, the novel RecGyp RS technique is a novel approach proposed that uses clustering and association rule mining to develop an efficient RS. The concept of clustering has been used keeping in mind the items that can be grouped based on the types and features of items [23]. Thus, clustering helps in classifying datasets and forming groups of items that may form "similar tastes".…”

Section: Techniquementioning

confidence: 99%

An Efficient Recommender System Technique in Social Networks Based on Association Rule Based Mining

Nandi¹

2019

IJITEE

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A recommender system is an information filtering system that has become a buzzword in various areas of marketing and research such as movies, music, books, products and research articles. The main role of recommender systems is to guide users on a personal level to provide an optimum set of suggestions based on the users’ taste, explicit rating of items, his/her demographic and other related valuable information. In the past decade, several approaches have been discussed for recommendation of items to online users keeping in mind the accuracy of prediction, the cold-start problem and the problem of sparsity. However, most of these existing recommender system techniques have failed to define a proper recommendation model that can be well suited for any online social network and can consider majority of the limitations when modeling real-market recommendations. In this paper, we present a novel efficient recommender system technique RecGyp and many other standard commonly used existing prediction techniques and also perform experimental evaluations to make a comparative analysis among each technique. The experiments carried out on the MovieLens 100K and the Yahoo! Webscope Movie datasets demonstrate the superior nature of the proposed RecGyp technique in solving the scalability issue and accuracy of results for recommending items to users of a social network. In addition to the traditional similarity measurements evaluation, results are also provided for three evaluation metrics, Precision, Recall and ROC, to evaluate the accuracy of all the recommender system techniques

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Section: Techniquementioning

confidence: 99%

An Efficient Recommender System Technique in Social Networks Based on Association Rule Based Mining

Nandi¹

2019

IJITEE

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“…Zhao et al [10] used visual tracking sensors to acquire biometric information and then used machine learning based biometrics to improve the accuracy of recognition. Combined with user attribute features, Al-Shamri [11] constructed five similarity measures, respectively, based on user preference modeling method, and the experimental results showed that the combination of user attribute features improves the recommendation accuracy of recommender systems. Santos et al [12] applied user attribute features in real recommendation environment to mine and analyze the context constraints in the scene.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Similarity Calculation Model

Fan

Chen

Zhu

et al. 2017

Scientific Programming

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This paper addresses the problems of similarity calculation in the traditional recommendation algorithms of nearest neighbor collaborative filtering, especially the failure in describing dynamic user preference. Proceeding from the perspective of solving the problem of user interest drift, a new hybrid similarity calculation model is proposed in this paper. This model consists of two parts, on the one hand the model uses the function fitting to describe users' rating behaviors and their rating preferences, and on the other hand it employs the Random Forest algorithm to take user attribute features into account. Furthermore, the paper combines the two parts to build a new hybrid similarity calculation model for user recommendation. Experimental results show that, for data sets of different size, the model's prediction precision is higher than the traditional recommendation algorithms.

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“…The user profile building process is therefore an extremely important step in order to obtain good personalized results. We can see the importance of building accurate user profiles even applied to other domains such as social media [21] or IR related fields such as recommender systems [3] .…”

Section: Related Workmentioning

confidence: 99%