Collaborative Filtering Recommender Systems

Ekstrand, Michael D.

doi:10.1561/1100000009

Cited by 807 publications

(370 citation statements)

References 126 publications

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“…First of all, a user rates some given items in an implicit or explicit fashion. Then, the recommender identifies the nearest neighbors whose tastes are similar to those of a given user and recommends items that the nearest neighbors have liked (Ekstrand et al 2011). CF is usually implemented on the basis of the following approaches: user-based (Asanov 2011), item-based (Sarwar et al 2001), model-based approaches (Koren et al 2009), and matrix factorization (Bokde et al 2015).…”

Section: Recommendation Techniques For Individualsmentioning

confidence: 99%

An overview of recommender systems in the healthy food domain

et al. 2017

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Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.

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Section: Recommendation Techniques For Individualsmentioning

confidence: 99%

An overview of recommender systems in the healthy food domain

et al. 2017

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“…In each dataset, users initially having less than 10 ratings were dropped, since users with few ratings are known to exhibit low accuracy in predictions computed for them [3]. This procedure did not affect the three MovieLens and the one NetFlix datasets, because these four datasets contain only users that have rated 20 items or more.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Afterwards, in order to predict the rating that u would give to an item i, that u has not reviewed yet, the ratings assigned to item i by u's NNs are combined [2], under the assumption that users are highly likely to exhibit similar tastes in the future, if they have done so in the past as well [3], [4]. To measure similarity between users, the Pearson Correlation Coefficient is the most commonly used formula in CF recommender systems.…”

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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“…Sung Ho Ha [17] used cluster analysis for customer segmentation and discovered customer segment knowledge to build a segment transition path, and then predicts customer segment behavior patterns. References [18][19][20] show the benefits of collaboration in recommender systems.…”

Section: Collaborative Filteringmentioning

confidence: 99%

A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems

2016

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This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005-2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.

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Collaborative Filtering Recommender Systems

Cited by 807 publications

References 126 publications

An overview of recommender systems in the healthy food domain

An overview of recommender systems in the healthy food domain

Using Time Clusters for Following Users’ Shifts in Rating Practices

A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems

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