Recommender Systems: Issues, Challenges, and Research Opportunities

Khusro, Shah; Ali, Zafar; Ullah, Irfan

doi:10.1007/978-981-10-0557-2_112

Cited by 128 publications

(42 citation statements)

References 24 publications

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“…The main assumption behind our proposed framework for Performance Evaluation of a Recommending Interface (PERI) is that different variants of a recommendation interface can have different impact on different users depending on their cognitive abilities [31,32], their way of interacting with a website and their goals of the visit to an e-commerce website. These assumptions have been confirmed by several studies [22,[33][34][35].…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing

Sulikowski

Zdziebko

2020

Electronics

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The increasing amount of marketing content in e-commerce websites results in the limited attention of users. For recommender systems, the way recommended items are presented becomes as important as the underlying algorithms for product selection. In order to improve the effectiveness of content presentation, marketing experts experiment with the layout and other visual aspects of website elements to find the most suitable solution. This study investigates those aspects for a recommending interface. We propose a framework for performance evaluation of a recommending interface, which takes into consideration individual user characteristics and goals. At the heart of the proposed solution is a deep neutral network trained to predict the efficiency a particular recommendation presented in a selected position and with a chosen degree of intensity. The proposed Performance Evaluation of a Recommending Interface (PERI) framework can be used to automate an optimal recommending interface adjustment according to the characteristics of the user and their goals. The experimental results from the study are based on research-grade measurement electronics equipment Gazepoint GP3 eye-tracker data, together with synthetic data that were used to perform pre-assessment training of the neural network.

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Section: Conceptual Frameworkmentioning

confidence: 99%

Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing

Sulikowski

Zdziebko

2020

Electronics

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“…and to infer information on users through data observed in similar users (Neighbourhood-based CF, memory-based algorithms). Once the estimates are reached for the X i vector of the u i user they are ordered in the ranking vector and displayed as recommendations (Koren and Bell 2015;Aggarwal 2016;Khusro et al 2016;Melville and Sindhwani 2017).…”

Section: Theoretical Paradigmmentioning

confidence: 99%

“…Recommender systems match data to the profile of a user, while search engines match data-structured answers to information science 'queries'. The value of these techniques is widely recognised but they require an adequate input of information and resources such as data storage and computational power (Varian 2016;Khusro et al 2016;Melville and Sindhwani 2017).…”

mentioning

confidence: 99%

Evaluating Rank-Coherence of Crowd Rating in Customer Satisfaction

Tomaselli

Cantone

2020

Soc Indic Res

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Crowd rating is a continuous and public process of data gathering that allows the display of general quantitative opinions on a topic from online anonymous networks as they are crowds. Online platforms leveraged these technologies to improve predictive tasks in marketing. However, we argue for a different employment of crowd rating as a tool of public utility to support social contexts suffering to adverse selection, like tourism. This aim needs to deal with issues in both method of measurement and analysis of data, and with common biases associated to public disclosure of rating information. We propose an evaluative method to investigate fairness of common measures of rating procedures with the peculiar perspective of assessing linearity of the ranked outcomes. This is tested on a longitudinal observational case of 7 years of customer satisfaction ratings, for a total amount of 26.888 reviews. According to the results obtained from the sampled dataset, analysed with the proposed evaluative method, there is a trade-off between loss of (potentially) biased information on ratings and fairness of the resulting rankings. However, computing an ad hoc unbiased ranking case, the ranking outcome through the time-weighted measure is not significantly different from the ad hoc unbiased case.

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“…In the event that the arbitrary number is bigger than the limit, parallel evaluations are turned around (1s are changed into 0s are changed into 1s) and sent to the CF frameworks [26]. For both of RRTs and RPTs, the number of arbitrarily chose unrated thing cells relies upon the measure of the unrated thing cells [27].…”

Section: B Randomization-basedmentioning

confidence: 99%

H2E: A Privacy Provisioning Framework for Collaborative Filtering Recommender System

Ashraf¹,

Naeem²,

Javed³

et al. 2019

IJMECS

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A Recommender System (RS) is the most significant technologies that handle the information overload problem of Retrieval Information by suggesting users with correct and related items. Today, abundant recommender systems have been developed for different fields and we put an effort on collaborative filtering (CF) recommender system. There are several problems in the recommender system such as Cold Start, Synonymy, Shilling Attacks, Privacy, Limited Content Analysis and Overspecialization, Grey Sheep, Sparsity, Scalability and Latency Problem. The current research explored the privacy in CF recommender system and defined the perspective privacy attributes (user's identity, password, address, and postcode/location) which are required to be addressed. Using the base models as Homomorphic and Hash Encryption scheme, we have proposed a hybrid model Homomorphic Hash Encryption (H2E) model that addressed the privacy issues according to defined objectives in the current study. Furthermore, in order to evaluate the privacy level, H2E was implementing in medicine recommender system and compared the consequences with existing state-of-the-art privacy protection mechanisms. It was observed that H2E outperform to other models with respect to determined privacy objectives. Leading to user's privacy, H2E can be considered a promising model for CF recommender systems.

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Recommender Systems: Issues, Challenges, and Research Opportunities

Cited by 128 publications

References 24 publications

Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing

Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing

Evaluating Rank-Coherence of Crowd Rating in Customer Satisfaction

H2E: A Privacy Provisioning Framework for Collaborative Filtering Recommender System

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