Dynamic generation of personalized hybrid recommender systems

Dooms, Simon

doi:10.1145/2507157.2508069

Cited by 26 publications

(14 citation statements)

References 10 publications

(2 reference statements)

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“…This is not necessarily a causal relationship, but may indicate that more active users in the system are more particularly interested in the recommender-switching feature. This finding is consistent with those of Dooms [6].…”

Section: Rq1: Users Switch Algorithmssupporting

confidence: 93%

“…In this work, we take a very basic approach to incorporating users' explicit feedback into the recommender selection process: we invite users to try different recommender algorithms and pick the one they want to use. This is similar to the work of Dooms, where users were given some control over their recommendations [6]; some results in that work parallel ours.…”

Section: Background and Related Worksupporting

confidence: 87%

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Letting Users Choose Recommender Algorithms

Ekstrand

Kluver

Harper

et al. 2015

Proceedings of the 9th ACM Conference on Recommender Systems

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Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a better or worse fit for different users and use cases. As one way of taking advantage of the relative merits of different algorithms, we gave users the ability to change the algorithm providing their movie recommendations and studied how they make use of this power. We conducted our study with the launch of a new version of the MovieLens movie recommender that supports multiple recommender algorithms and allows users to choose the algorithm they want to provide their recommendations. We examine log data from user interactions with this new feature to understand whether and how users switch among recommender algorithms, and select a final algorithm to use. We also look at the properties of the algorithms as they were experienced by users and examine their relationships to user behavior.We found that a substantial portion of our user base (25%) used the recommender-switching feature. The majority of users who used the control only switched algorithms a few times, trying a few out and settling down on an algorithm that they would leave alone. The largest number of users prefer a matrix factorization algorithm, followed closely by item-item collaborative filtering; users selected both of these algorithms much more often than they chose a non-personalized mean recommender. The algorithms did produce measurably different recommender lists for the users in the study, but these differences were not directly predictive of user choice.

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Section: Rq1: Users Switch Algorithmssupporting

confidence: 93%

Section: Background and Related Worksupporting

confidence: 87%

Letting Users Choose Recommender Algorithms

Ekstrand

Kluver

Harper

et al. 2015

Proceedings of the 9th ACM Conference on Recommender Systems

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show abstract

“…Besides awareness and explanation, allowing users to take control of the filtering process and its outcome is also important (Dooms 2013;Loepp et al 2014;Mcnee et al 2003). User's control can not only provide feedback to the filtering algorithm for adjustment, but also increase user's satisfaction, as well as persuasiveness of the recommended messages through a sense of control and confidence (Cleger-Tamayo et al 2013;Tintarev and Masthoff 2012).…”

Section: Necessity Of Providing Awareness Explanation and Control Inmentioning

confidence: 99%

“…The importance of awareness and explanation has been discussed in previous studies (Scheel et al 2014;Johnson and Johnson 1993), that lack of such transparency can lead to reduced user satisfaction (e.g., trust toward personalized filtering) in the system (Sinha and Swearingen 2002;Kay and Kummerfeld 2013;Lim et al 2009). Therefore, it is necessary to allow users to take control of the filtering process and its outcome (Dooms 2013;Loepp et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Providing awareness, explanation and control of personalized filtering in a social networking site

Nagulendra

Vassileva

2015

Inf Syst Front

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Social networking sites (SNSs) have applied personalized filtering to deal with overwhelmingly irrelevant social data. However, due to the focus of accuracy, the personalized filtering often leads to Bthe filter bubble^problem where the users can only receive information that matches their pre-stated preferences but fail to be exposed to new topics. Moreover, these SNSs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user's usage experience and trust in the system can decline. This paper presents an interactive method to visualize the personalized filtering in SNSs. The proposed visualization helps to create awareness, explanation, and control of personalized filtering to alleviate the Bfilter bubble^problem and increase the users' trust in the system. Three user evaluations are presented. The results show that users have a good understanding about the filter bubble visualization, and the visualization can increase users' awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing. The intuitiveness of the design is overall good, but a context sensitive help is also preferred. Moreover, the visualization can provide users with better usage experience and increase users' trust in the system.

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“…Considerable amount of techniques have been proposed to design the user model in terms of resource recommendations [21] [8]. Among them, some approaches aim to provide dynamically adapted personalized recommendations to users [13].…”

Section: Related Workmentioning

confidence: 99%

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

Seitlinger

Kowald

Kopeinik

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

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Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting nonlinear user-resource dynamics shaping attention and interpretation. In this paper, we propose a novel hybrid recommendation strategy that refines CF by capturing these dynamics. The evaluation results reveal that our approach substantially improves CF and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant.

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Dynamic generation of personalized hybrid recommender systems

Cited by 26 publications

References 10 publications

Letting Users Choose Recommender Algorithms

Letting Users Choose Recommender Algorithms

Providing awareness, explanation and control of personalized filtering in a social networking site

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

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