A fuzzy model for managing natural noise in recommender systems
Graphical abstract
Introduction
The development of the e-commerce has made available huge quantities of information and items that customers cannot filter effectively. Therefore, the facilitation of finding out quick and easily items that fits their preferences and needs is an important challenge nowadays. Recommender systems (RS) are probably the most successful tool to support personalised recommendations [1], [2]. Currently, they are widely used in different scenarios like e-commerce [3], e-learning [4], [5], e-government [6], tourism [7], [8], web pages [9], and digital games [10].
Different approaches have been used in RS, being the content-based (CBRS) [11] and the collaborative filtering (CFRS) [1] the most widespread. CBRS methods are based on items’ descriptions to generate the users’ recommendations, meanwhile CFRS have performed this task just using users ratings about items.
CFRS are currently the most popular type of RS in real world because they perform very well even when items descriptions are not available. In spite of this, the necessity of customers preferences has produced some problems that limit their performance, such as cold start and sparsity [1], [12], and more recently new related problems regarding the quality of the rating data [13], [14], [15]. Specifically, Ekstrand et al. [16] pointed out that the rating elicitation process is not error-free, hence the ratings can contain noise. They mentioned that such a noise, previously coined natural noise in [17], could be caused by human error, mixing of factors in the rating process, uncertainty and other factors. They stated that its detection and correction should provide more accurate recommendations.
Thereby several approaches have been introduced for managing these rating inconsistencies in recommendation scenarios depending on the information available, such as user dependent approaches [14], item-attributes dependent approaches [18], and also approaches that manage natural noise only using the rating values [15]. They all perform generally better that base algorithms, but they present an important limitation to deal with inherent uncertainty and vagueness of preferences because they solely represent and manage them by means of crisp values that may imply lack of robustness.
Therefore, this paper aims at developing a more flexible approach for dealing with natural noise in RS, that properly models such vagueness and uncertainty by means of fuzzy tools such as fuzzy sets [19], fuzzy linguistic approach [20], [21] and computing with words [22], [23] that have provided successful results modelling that type of uncertainty in other problems [24], [25], [26]. Hence, the main objective of this paper is to improve accuracy of RS by managing natural noise with a novel fuzzy-based method that manages the uncertainty present in natural noise by using fuzzy tools [19] that provide a greater flexibility in the characterisation process of elements in the recommendation system, leading to improvements in the recommendation accuracy.
The remainder of this paper is structured as follows. First, Section 2 provides the required background for the current research. Section 3 focuses on the novel fuzzy approach for dealing with natural noise in CFRS. Section 4 then presents an experimental procedure to evaluate the performance of the approach in relation to the previous works. Finally, Section 5 concludes the article and remarks further work.
Section snippets
Preliminaries
This section provides the required background for the current research, including basics about CFRS, a review of natural noise processing in CFRS, and a description of the fuzzy logic tools used in the proposal.
A fuzzy approach to detect noisy ratings
Natural noise in RS may bias recommendations, here it is introduced a new fuzzy approach to deal with it that unlike previous rigid proposals, this new approach uses fuzzy tools for managing in a more flexible way the inherent uncertainty of customers preferences/needs. The general scheme of this fuzzy approach for managing natural noise is depicted in Fig. 1 (amplified in Fig. 2, Fig. 5, Fig. 6) and consists of the following steps:
- (a)
Fuzzy profiling: To manage the uncertainty of ratings, they are
Case study
This section describes the experiments developed to evaluate the effect of our proposal on the improvement of the recommendation accuracy. Hence our proposal is specified and then compared with other methods that deal with noise in the ratings. First, the datasets (MovieLens, MovieTweeting and Netflix Tiny) in which experiments have been performed are detailed in Section 4.1. Then a specification of the fuzzy method applied in the case study is provided in Section 4.2. After that, the
Concluding remarks
Natural noise is denominated to the noise unintentionally introduced by human beings when they are eliciting preferences. Its management in recommender systems has attracted the attention of many researchers and several proposals have been done. However, such proposals deal with natural noise in a rigid way such an small variation can make change the classification of ratings as noisy or not noisy.
Therefore, to avoid such a strict managing process, this paper has introduced a more flexible
Acknowledgements
This research work was partially supported by the Research Project TIN-2012-31263, the Eureka SD Project (agreement number 2013-2591), which is supported by the Erasmus Mundus Programme of the European Union, and also the Spanish Ministry of Education, Culture and Sport FPU fellowship (FPU13/01151).
References (61)
- et al.
RSS-based e-learning recommendations exploiting fuzzy FCA for knowledge modeling
Appl. Soft Comput.
(2012) - et al.
A mobile 3D-GIS hybrid recommender system for tourism
Inf. Sci.
(2012) - et al.
Building an expert travel agent as a software agent
Expert Syst. Appl.
(2009) - et al.
Newer: a system for neuro-fuzzy web recommendation
Appl. Soft Comput.
(2011) - et al.
Improving a simulated soccer team's performance through a memory-based collaborative filtering approach
Appl. Soft Comput.
(2014) - et al.
A fuzzy model to evaluate the suitability of installing an ERP system.
Inf. Sci.
(2009) - et al.
Using SVD and demographic data for the enhancement of generalized collaborative filtering
Inf. Sci.
(2007) - et al.
Recommender systems survey
Knowl.-Based Syst.
(2013) - et al.
An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics
Inf. Sci.
(2013) Fuzzy sets
Inf. Control
(1965)
An overview on the 2-tuple linguistic model for computing with words in decision making: extensions, applications and challenges
Inf. Sci.
Linguistic decision making: tools and applications
Inf. Sci.
Combining instance selection methods based on data characterization: an approach to increase their effectiveness
Inf. Sci.
Multi-label classification based on analog reasoning
Expert Syst. Appl.
A Hooke's law-based approach to protein folding rate
J. Theor. Biol.
A statistical comparative study of different similarity measures of consensus in group decision making
Inf. Sci.
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
IEEE Trans. Knowl. Data Eng.
Recommender systems: from algorithms to user experience
User Model. User-Adapt. Interact.
E-commerce product recommendation agents: use, characteristics, and impact
Manag. Inf. Syst. Q.
An e-learning collaborative filtering approach to suggest problems to solve in programming online judges
Int. J. Distance Educ. Technol.
Intelligent e-government services with personalized recommendation techniques
Int. J. Intell. Syst.
Content-based recommendation systems
Adapt. Web
Using linguistic incomplete preference relations to cold start recommendations
Internet Res.
Noisy but non-malicious user detection in social recommender systems
World Wide Web
Preference-based user rating correction process for interactive recommendation systems
Multimed. Tools Appl.
Correcting noisy ratings in collaborative recommender systems
Knowl.-Based Syst.
Collaborative filtering recommender systems
Found. Trends Hum.-Comput. Interact.
Detecting noise in recommender system databases
Data mining methods for recommender systems
Recommender Systems Handbook
Cited by (55)
Extending a human error identification and assessment method considering the uncertainty information for human reliability analysis of robot-assisted rehabilitation
2024, Engineering Applications of Artificial IntelligenceSampling and noise filtering methods for recommender systems: A literature review
2023, Engineering Applications of Artificial IntelligenceA fuzzy content-based group recommender system with dynamic selection of the aggregation functions
2022, International Journal of Approximate ReasoningA Mixture-of-Gaussians model for estimating the magic barrier of the recommender system[Formula presented]
2022, Applied Soft ComputingCitation Excerpt :Human inconsistency is often caused by the changes in human emotions and living environments [7]. Human error causes the malicious noise [8], while the latter two arise the natural noise [9]. We will discuss the natural noise throughout the paper.
Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approach
2022, Expert Systems with ApplicationsAn effective and efficient fuzzy approach for managing natural noise in recommender systems
2021, Information SciencesCitation Excerpt :In our scheme, we devise a membership function to classify the ratings based on unfixed boundary points. To verify the effectiveness of unfixed boundary points in our membership function, we compare our membership function with the membership function in [16], which is based on fixed boundary points. The same schemes (i.e., the detecting scheme in Section 3.3 and the correction schemes in Section 3.4) are used here.