The MovieLens Datasets

Harper, F. Maxwell; Konstan, Joseph A.

doi:10.1145/2827872

Cited by 2,450 publications

(461 citation statements)

References 34 publications

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“…For low diversity, the algorithm simply selected the N items closest to the centroid. To test our algorithm we ran several simulations and tests using the 10 M movielens (Harper and Konstan 2015) dataset. As an initial starting set, the Top-200, or the 200 items with highest predicted rating was found to provide a good balance between maximum range in predicted rating and maximum diversity.…”

Section: Diversification Algorithmmentioning

confidence: 99%

Understanding the role of latent feature diversification on choice difficulty and satisfaction

Willemsen

Graus

Knijnenburg

2016

User Model User-Adap Inter

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People like variety and often prefer to choose from large item sets. However, large sets can cause a phenomenon called "choice overload": they are more difficult to choose from, and as a result decision makers are less satisfied with their choices. It has been argued that choice overload occurs because large sets contain more similar items. To overcome this effect, the present paper proposes that increasing the diversity of item sets might make them more attractive and satisfactory, without making them much more difficult to choose from. To this purpose, by using structural equation model methodology, we study diversification based on the latent features of a matrix factorization recommender model. Study 1 diversifies a set of recommended items while controlling for the overall quality of the set, and tests it in two online user experiments with a movie recommender system. Study 1a tests the effectiveness of the latent feature diversification, and shows that diversification increases the perceived diversity and attractiveness of the item set, while at the same time reducing the perceived difficulty of choosing from the set. Study 1b subsequently shows that diversification can increase users' satisfaction with the chosen option, especially when they are choosing from small, diverse item sets. Study 2 extends these results by testing our diversification algorithm against traditional Top-N recommendations, and finds that diverse, The acting editor for the review of this article has been Dietmar Jannach, Technical University of Dortmund, Germany. small item sets are just as satisfying and less effortful to choose from than Top-N recommendations. Our results suggest that, at least for the movie domain, diverse small sets may be the best thing one could offer a user of a recommender system.

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Section: Diversification Algorithmmentioning

confidence: 99%

Understanding the role of latent feature diversification on choice difficulty and satisfaction

Willemsen

Graus

Knijnenburg

2016

User Model User-Adap Inter

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“…In our experiments, we have used the publicly available MovieLens 1M dataset [25]. This dataset consists of 1,000,209 ratings by 6040 MovieLens users of approximately 3900 movies, which contains all genuine users.…”

Section: Datasetmentioning

confidence: 99%

“…In this paper, we propose a technique to detect profile injection attacks using Random Forest Classifier and conduct experiments on the MovieLens dataset [25] -1M to verify the effectiveness of the proposed approach by comparing it with different classifier techniques. The paper is organized as follows: Section 2 describes the related work, in Section 3 we discuss details about our proposed approach, Section 4 deals with the experiments performed and their analysis and finally in Section 5 we conclude the paper along with the possible future scope.…”

Section: Introductionmentioning

confidence: 99%

An Obfuscated Attack Detection Approach for Collaborative Recommender Systems

Kapoor

Gupta

Kumar

2018

CIT

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In the recent times, we have loads and loads of information available over the Internet. It has become very cumbersome to extract relevant information out of this huge amount of available information. So to avoid this problem, recommender systems came into play, which can predict outcomes according to user's interests. Although recommender systems are very effective and useful for users, the most used type of recommender system, i.e. collaborative filtering recommender system, suffers from shilling/profile injection attacks in which fake profiles are inserted into the database in order to bias its output. With this problem in mind,we propose an approach to detect attacks on recommender systems using Random Forest Classifier and find that, when tested at 10% attack, our approach outperformed earlier proposed approaches.

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“…Four of these datasets are obtained from Amazon [8], [9], three from MovieLens [10], [11], and one from Netflix [12]. These eight datasets used in our experiments (a) contain reliable timestamps (most of the ratings within each dataset have been entered in real rating time and not in a batch mode), (b) are up to date (published between 1998 and 2016), (c) are widely used as benchmarking datasets in CF research and (d) vary with respect to type of dataset (movies, music, videogames and books) and size (from 2MB, up to 4.7GB).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The proposed algorithm, as well as the two algorithms presented in [7], are based on the exploitation of timestamp information which is associated with ratings; hence in this work, we use the Amazon datasets [8], [9], the MovieLens datasets [10], [11] and the Netflix dataset [12], which include the ratings' timestamps. It is worth noting that the proposed algorithm can be combined with other techniques that have been proposed for either improving prediction accuracy in CF-based systems, including consideration of social network data (e.g.…”

Section: Introductionmentioning

confidence: 99%

Using Time Clusters for Following Users’ Shifts in Rating Practices

Margaris

Vassilakis

2017

CSIMQ

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Abstract. Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user's rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users' ratings is inadequate, since it fails to follow such shifts in users' rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in useruser collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users' rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.

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The MovieLens Datasets

Cited by 2,450 publications

References 34 publications

Understanding the role of latent feature diversification on choice difficulty and satisfaction

Understanding the role of latent feature diversification on choice difficulty and satisfaction

An Obfuscated Attack Detection Approach for Collaborative Recommender Systems

Using Time Clusters for Following Users’ Shifts in Rating Practices

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