A fuzzy approach for natural noise management in group recommender systems

doi:10.1016/j.eswa.2017.10.060

Expert Systems with Applications

Volume 94, 15 March 2018, Pages 237-249

https://doi.org/10.1016/j.eswa.2017.10.060 Get rights and content

Highlights

•
Natural noise management for collaborative filtering-based group recommendation.
•
Novel fuzzy approach for natural noise management for group recommender systems.
•
Greater flexibility, robustness of noise management for group recommender systems.
•
Case study on well-known recommendation datasets in the movies domain.
•
Impact of the proposal regarding the group size, aggregation approach and strategy.

Abstract

Information filtering is a key task in scenarios with information overload. Group Recommender Systems (GRSs) filter content regarding groups of users preferences and needs. Both the recommendation method and the available data influence recommendation quality. Most researchers improved group recommendations through the proposal of new algorithms. However, it has been pointed out that the ratings are not always right because users can introduce noise due to factors such as context of rating or user’s errors. This introduction of errors without malicious intentions is named natural noise, and it biases the recommendation. Researchers explored natural noise management in individual recommendation, but few explored it in GRSs. The latter ones apply crisp techniques, which results in a rigid management. In this work, we propose Natural Noise Management for Groups based on Fuzzy Tools (NNMG-FT). NNMG-FT flexibilises the detection and correction of the natural noise to perform a better removal of natural noise influence in the recommendation, hence, the recommendations of a latter GRS are then improved.

Introduction

The Web allows people accessing to a huge amount of information. However, the users skills to cope with all the available information are limited, which leads to select suboptimal alternatives. This problem is known as information overload. Recommender Systems (RSs) are tools to help individuals to overcome such information overload problem personalizing access to information (Adomavicius, Tuzhilin, 2005, Ekstrand, Riedl, Konstan, 2011). However, some items tend to be consumed by groups of users, such as tourist attractions (Garcia, Pajares, Sebastia, & Onaindia, 2012) or television programmes (Said, Berkovsky, & De Luca, 2011). With this purpose in mind, Group Recommender Systems (GRSs) (Masthoff, 2015) help groups of users to find suitable items according to their preferences and needs.

Several techniques have been used to improve individual recommendation, such as neighborhood-based collaborative filtering (Sarwar, Karypis, Konstan, & Riedl, 2001), matrix factorisation (Koren, Bell, & Volinsky, 2009), or approaches that consider temporal dynamics (Koren, 2010, Rafailidis, Kefalas, Manolopoulos, 2017). In the case of group recommendation, there are approaches to aggregate individual information (Masthoff, 2015), to consider consensus among members (Castro, Quesada, Palomares, & Martínez, 2015), or matrix factorisation models for groups (Ortega, Hernando, Bobadilla, & Kang, 2016).

A decade ago, it was pointed out that explicitly stated user preferences may not be error free (O’Mahony, Hurley, & Silvestre, 2006). More recently, other recent works (Bellogín, Said, de Vries, 2014, Centeno, Hermoso, Fasli, 2015, Guo, Dunson, 2015, Zhang, Zhao, Lui, 2017) have also pointed out that a person’s ratings are noisy, inconsistent, and biased. Li, Chen, Zhu, and Zhang (2013) determined that too many noisy ratings can distort users’ preference profiles, which result in unlike-minded neighbors that imply a quality loss in recommendations. Kluver, Nguyen, Ekstrand, Sen, and Riedl (2012) have also suggested that user ratings are imperfect and noisy, and such noise limits the predictive power of any RS.

Therefore, in addition to improving recommendations through new recommendation approaches, researchers should also focus on improving the quality of the rating database (Amatriain, Pujol, Tintarev, & Oliver, 2009c). In RSs, there are two kinds of noise in the database (O’Mahony et al., 2006): (i) malicious noise, that consists of erroneous data deliberately inserted in the system to influence recommendations, and (ii) natural noise, that appears when users unpurposely introduce erroneous data due to human errors or external factors during the rating process. This paper focuses on the latter.

Natural noise biases recommendations, therefore, its management is a key factor to improve them. There are several Natural Noise Management (NNM) approaches for individual RSs databases. While some NNM approaches need additional information (Amatriain, Lathia, Pujol, Kwak, Oliver, 2009a, Pham, Jung, 2013), others detect and correct the natural noise using information already contained in the database (Yera, Castro, Martínez, 2016, Yera Toledo, Caballero Mota, Martínez, 2015).

GRSs also rely on databases with explicit users’ preferences (Masthoff, 2015), therefore, they are affected by natural noise. Castro, Yera, and Martínez (2017) propose a NNM approach for GRSs to manage ratings and noise using crisp values. This is the only work focused on NNM in GRSs. However, the crisp management is not either flexible or robust enough to deal with the uncertainty and vagueness of both the ratings and the NNM, which makes it necessary to develop new proposals with this regard.

In order to manage such uncertainty and vagueness in RSs contexts, the use of fuzzy tools has been considered for several years. A recent survey paper (Yera & Martínez, 2017) has shown that some traditional fuzzy tools have been successfully used for a more flexible and accurate information processing in RSs. However, it also shows that there are several research gaps related to the necessity of new fuzzy approaches focused on the use of emergent information sources and concentrated in new research trends in RSs. Specifically, the natural noise management (Martínez, Castro, & Yera, 2016) is one of such research trends. Our purpose is to study the natural noise management in group recommendation with fuzzy tools.

Therefore, in this work we propose Natural Noise Management for Groups based on Fuzzy Tools (NNMG-FT) to improve the rating database removing the natural noise. NNMG-FT applies three steps of management: fuzzy profiling, global noise management and local noise management. Both global natural noise management step and local noise management step are divided into two sub-steps: noise detection and noise correction. Both sub-steps apply fuzzy tools. In the noise detection, fuzzy tools allow to make a flexible classification of the ratings into noisy or not noisy. In the noise correction, this flexible classification is used to correct noisy ratings applying a soft modification of the value regarding its noise degree. The main advantages of NNMG-FT are: flexibility, robustness and consideration of group information in the NNM. A case study was performed to show the validity of NNMG-FT.

In short, the main contributions of this paper consist of:

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Design an improved profiling that manages uncertainty and vagueness of the ratings through the application of fuzzy tools in the profiling of ratings, users, and items.
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Design an adequate representation of the noise management process that improves the flexibility and robustness of the noise detection and noise correction.
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Propose a NNM approach for GRSs that hybridizes several steps of noise detection and correction based on the information level from the viewpoints of both the whole ratings database and the groups ratings.
•
Validate the proposal through comparison with previous ones with similar purpose.

The remainder of this paper is structured as follows. First, Section 2 presents the related works for the current research. Section 3 details NNMG-FT, our proposal for NNM in group recommendation. Section 4 shows the case study done to validate NNMG-FT performance. Finally, Section 5 concludes the work.

Section snippets

Related works

In this section we revise different concepts about natural noise management in recommender systems, GRSs, and fuzzy sets, that are used in our NNM approach for GRSs.

Natural noise management for groups based on fuzzy tools

Previous approach for GRS with NNM (Castro et al., 2017) does not manage the inherent uncertainty and vagueness of the noise. We aim to fill this gap by proposing an approach for NNM for Groups based on Fuzzy Tools (NNMG-FT).

NNMG-FT analyses the rating database to detect noisy ratings and correct them to reduce their impact in a latter GRS. NNMG-FT has three main phases, as Fig. 1 shows:

1.
Fuzzy profiling: Generates a representation for users, items and ratings to characterise them and facilitate

Case study

We developed a case study to evaluate NNMG-FT. The remaining of this section details the experimental protocol and shows its results.

Conclusions

This paper proposes a natural noise management approach for group recommender systems using fuzzy tools (NNMG-FT). Specifically, NNMG-FT uses fuzzy profiles to characterise the rating tendency of users and items. With this characterisation, ratings that do not follow their corresponding user and item tendency are identified as noisy and, therefore, corrected. NNMG-FT performs two phases of noise correction: the first one follows a global approach, and the second is personalised to the target

Acknowledgements

This paper was partially supported by the Spanish FPU fellowship (FPU13/01151), the Spanish National research project TIN2015-66524-P.

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View full text

A fuzzy approach for natural noise management in group recommender systems

Highlights

Abstract

Introduction

Section snippets

Related works

Natural noise management for groups based on fuzzy tools

Case study

Conclusions

Acknowledgements

Decision Support Systems

Journal of Intelligent Information Systems

Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions

IEEE Transactions on Knowledge and Data Engineering

The wisdom of the few: A collaborative filtering approach based on expert opinions from the web

32nd international ACM SIGIR conference

I like it...i like it not: Evaluating user ratings noise in recommender systems

User modeling, adaptation, and personalization

Rate it again: Increasing recommendation accuracy by user re-rating

Third ACM conference on recommender systems

Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices

Applied Artificial Intelligence

The magic barrier of recommender systems–no magic, just ratings

International conference on user modeling, adaptation, and personalization

A consensus-driven group recommender system

International Journal of Intelligent Systems

On the inaccuracy of numerical ratings: Dealing with biased opinions in social networks

Information Systems Frontiers

Flytrap: Intelligent group music recommendation

Proceedings of the 7th international conference on intelligent user interfaces

Comparison of group recommendation algorithms

Multimedia Tools and Applications

Collaborative filtering recommender systems

Foundations and Trends in Human-Computer Interaction

Preference elicitation techniques for group recommender systems

Information Sciences

Evaluating collaborative filtering recommender systems

ACM Transactions on Information Systems

A 2-tuple fuzzy linguistic representation model for computing with words

IEEE Transactions on Fuzzy Systems

Virtual user approach for group recommender systems using precedence relations

Information Sciences

Collaborative filtering with temporal dynamics

Interacting with Computers of the ACM

Matrix factorization techniques for recommender systems

Computer

Noisy but non-malicious user detection in social recommender systems

World Wide Web

Managing natural noise in recommender systems