A study of the dynamic features of recommender systems

Rana, Chhavi; Jain, Sanjay K.

doi:10.1007/s10462-012-9359-6

Cited by 55 publications

(39 citation statements)

References 68 publications

(70 reference statements)

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“…We use recommender dynamic here to broadly refer to the change in recommendations. There are many kinds of dynamics in dynamical recommender systems [24]. The most classic ones model users' temporal preference drifting [13,8,3].…”

Section: Related Workmentioning

confidence: 99%

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Toward Better Interactions in Recommender Systems

Zhao

Adomavicius

Harper

et al. 2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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Current recommender systems often show the same mosthighly recommended items again and again ignoring the feedback that users neither rate nor click on those items. We conduct an online field experiment to test two ways of manipulating top-N recommendations with the goal of improving user experience: cycling the top-N recommendation based on their past presentation and serpentining the top-N list mixing the best items into later recommendation requests. We find interesting tensions between opt-outs and activities, user perceived accuracy and freshness. Cycling within the same session might be a "love it or hate it" recommender property because users in it have a higher opt-out rate but engage in more activities. Cycling across sessions and serpentining increase user activities without significantly affecting opt-out rates. Users perceive more change and freshness but less accuracy and familiarity. Combining cycling and serpentining does not work as well as each individual manipulation separately. These two ways of manipulations on top-N list demonstrate some attractive properties but also call for innovative approaches to overcome their potential costs.

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Section: Related Workmentioning

confidence: 99%

“…The most classic ones model users' temporal preference drifting [13,8,3]. Rana and Jain [24] classified the dynamics of recommender systems into six categories: temporal changes, online processing, context, novelty, serendipity, and diversity. We review dynamics in recommender systems from a different perspective here.…”

Section: Related Workmentioning

confidence: 99%

Toward Better Interactions in Recommender Systems

Zhao

Adomavicius

Harper

et al. 2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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“…A good RS takes into account the changing system's contents and the dynamic users' preferences. Rana and Jain [76] explore various parameters which are important for the dynamism in RS. These parameters are: serendipity, novelty, temporal characteristic, context, dynamic environment and diversity.…”

Section: Recommender Systemsmentioning

confidence: 99%

An Application-oriented Review of Deep Learning in Recommender Systems

Shokeen¹,

Rana²

2019

IJISA

Self Cite

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The development in technology has gifted huge set of alternatives. In the modern era, it is difficult to select relevant items and information from the large amount of available data. Recommender systems have been proved helpful in choosing relevant items. Several algorithms for recommender systems have been proposed in previous years. But recommender systems implementing these algorithms suffer from various challenges. Deep learning is proved successful in speech recognition, image processing and object detection. In recent years, deep learning has been also proved effective in handling information overload and recommending items. This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications. The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.

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“…Thus the approximated correlation coefficient is given by: i is a movie with a relatively high standard, so its rating is 0.5 stars higher than the average rating. In addition, the movie solves the problem by solving the least-squares problem [9][10][11] . The cost function formula is as follows: Because the proposed model of this paper is different from the traditional one, the data matrix cannot be directly applied to the training of the model.…”

Section: Improved Similarity Measuresmentioning

confidence: 99%

A Collaborative Filtering Recommendation Algorithm with Improved Similarity Calculation

Yang

Liu

Zhao

2018

International Journal of Advanced Network, Monitoring and Controls

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Abstract-In order to improve the accuracy of the proposed algorithm in collaborative filtering recommendation system, an Improved Pearson collaborative filtering (IP-CF) algorithm is proposed in this paper. The algorithm uses the user portrait, item characteristics and data of user behavior to compute the baseline predictors model. Instead of the traditional algorithm's similarity calculation, the prediction model is used to improve the accuracy of the recommendation algorithm. Experimental results on Moivelens dataset show that the IP-CF algorithm significantly improves the accuracy of the recommended results, and the RMSE and MAE evaluation results are better than the traditional algorithms.

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A study of the dynamic features of recommender systems

Cited by 55 publications

References 68 publications

Toward Better Interactions in Recommender Systems

Toward Better Interactions in Recommender Systems

An Application-oriented Review of Deep Learning in Recommender Systems

A Collaborative Filtering Recommendation Algorithm with Improved Similarity Calculation

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