A Survey of E-Commerce Recommender Systems

Wei, Kangning; Huang, Jinghua; Fu, Shaohong

doi:10.1109/icsssm.2007.4280214

Cited by 158 publications

(93 citation statements)

References 28 publications

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“…4.2. We first analyze the rationality of the proposed feature extraction method, then show the detection accuracy of the proposed detection method.…”

Section: Practical Value Of the Proposed Detection Methodsmentioning

confidence: 99%

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Shilling Attack Detection in Recommender Systems via Selecting Patterns Analysis

Gao

Hua

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYCollaborative filtering (CF) has been widely used in recommender systems to generate personalized recommendations. However, recommender systems using CF are vulnerable to shilling attacks, in which attackers inject fake profiles to manipulate recommendation results. Thus, shilling attacks pose a threat to the credibility of recommender systems. Previous studies mainly derive features from characteristics of item ratings in user profiles to detect attackers, but the methods suffer from low accuracy when attackers adopt new rating patterns. To overcome this drawback, we derive features from properties of item popularity in user profiles, which are determined by users' different selecting patterns. This feature extraction method is based on the prior knowledge that attackers select items to rate with man-made rules while normal users do this according to their inner preferences. Then, machine learning classification approaches are exploited to make use of these features to detect and remove attackers. Experiment results on the MovieLens dataset and Amazon review dataset show that our proposed method improves detection performance. In addition, the results justify the practical value of features derived from selecting patterns.

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“…4.2. We first analyze the rationality of the proposed feature extraction method, then show the detection accuracy of the proposed detection method.…”

Section: Practical Value Of the Proposed Detection Methodsmentioning

confidence: 99%

“…The MovieLens100K dataset consists of the ratings from 943 users on 1682 items, with a rating frequency not less than 20 for each item [4]. The Amazon review dataset is crawled from Amazon.cn till August 20, 2012, which contains 1205125 reviews written by 645072 reviewers on 136785 products [32].…”

Section: Experimental Methodsmentioning

confidence: 99%

Shilling Attack Detection in Recommender Systems via Selecting Patterns Analysis

Gao

Hua

et al. 2016

IEICE Trans. Inf. & Syst.

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“…Movie recommendation websites are probably the most well-known cases to users and are without a doubt the most well studied by researchers (Antonopoulus & Salter, 2006;Konstan, Miller, & Riedl, 2004;Li & Yamada, 2004), although there are many other fields in which RS have great and increasing importance, such as e-commerce (Jinghua, Kangning, & Shaohong, 2007) and e-learning (Bobadilla, Serradilla, & Hernando, 2009;Denis, 2007).…”

Section: Introductionmentioning

confidence: 99%

A collaborative filtering similarity measure based on singularities

Bobadilla

Ortega

Hernando

2012

Information Processing & Management

150

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Recommender systems play an important role in reducing the negative impact of information overload on those websites where users have the possibility of voting for their preferences on Ítems. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to calcúlate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corrobórate the excellent behaviour of the singularity measure proposed.

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“…Nevertheless, RS have been introduced to a greater extent in the collaborative systems environment and they cover a large number of applications [4][5] [6] in which it is useful to receive recommendations based on the preferences of a group of users with similar tastes or needs to those of each individual that makes use of these systems.…”

Section: Introductionmentioning

confidence: 99%

Choice of metrics used in collaborative filtering and their impact on recommender systems

Sánchez¹,

Serradilla²,

Martinez³

et al. 2008

2008 2nd IEEE International Conference on Digital Ecosystems and Technologies

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Abstract-The capacity of recommender systems to make correct predictions is essentially determined by the quality and suitability of the collaborative filtering that implements them. The common memory-based metrics are Pearson correlation and cosine, however, their use is not always the most appropriate or sufficiently justified. In this paper, we analyze these two metrics together with the less common mean squared difference (MSD) to discover their advantages and drawbacks in very important aspects such as the impact when introducing different values of k-neighborhoods, minimization of the MAE error, capacity to carry out a sufficient number of predictions, percentage of correct and incorrect predictions and behavior when attempting to recommend the n-best items. The paper lists the results and practical conclusions that have been obtained after carrying out a comparative study of the metrics based on 135 experiments on the MovieLens database of 100,000 ratios.

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A Survey of E-Commerce Recommender Systems

Cited by 158 publications

References 28 publications

Shilling Attack Detection in Recommender Systems via Selecting Patterns Analysis

Shilling Attack Detection in Recommender Systems via Selecting Patterns Analysis

A collaborative filtering similarity measure based on singularities

Choice of metrics used in collaborative filtering and their impact on recommender systems

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