Performance of recommender algorithms on top-n recommendation tasks

Cremonesi, Paolo; Koren, Yehuda; Turrin, Roberto

doi:10.1145/1864708.1864721

Cited by 1,134 publications

(972 citation statements)

References 15 publications

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“…Instead of the standard recall metric used by [3,11], which considers a subset of the top rated items, we defined yet another recall based metric -Target Recall (TRecall) -which contemplates a subset of items centered around the target rating. TRecall evaluates the accuracy of the predictions when compared with the actual viewer ratings.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Regarding the positive feedback, we use the Recall proposed by Cremonesi [3], and for each new viewer rating event, we determine the Recall@N. First, we predict the ratings of all items unseen by the viewer, including the new rated item, then we select 1000 unrated items plus the new rated item and sort them in descending order. Finally, if the newly rated item belongs to the list of the top N viewer predicted items, we count a hit.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Personalised Dynamic Viewer Profiling for Streamed Data

Veloso

Malheiro

Burguillo

et al. 2018

Advances in Intelligent Systems and Computing

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Nowadays, not only the number of multimedia resources available is increasing exponentially, but also the crowd-sourced feedback volunteered by viewers generates huge volumes of ratings, likes, shares and posts/reviews. Since the data size involved surpasses human filtering and searching capabilities, there is the need to create and maintain the profiles of viewers and resources to develop recommendation systems to match viewers with resources. In this paper, we propose a personalised viewer profiling technique which creates individual viewer models dynamically. This technique is based on a novel incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations.

show abstract

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Personalised Dynamic Viewer Profiling for Streamed Data

Veloso

Malheiro

Burguillo

et al. 2018

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

show abstract

“…Around the same time, Valve, the company behind Steam, introduced a recommender system to their storefront (the two projects being unrelated). The work focused on recommending games, similar to movie recommendations on platforms such as Netflix or app recommendations on the AppStore [16], [17]. Similarly, Anwar et al [18] used collaborative filtering to suggest games to players via evaluating the opinions of similar players.…”

Section: Related Workmentioning

confidence: 99%

Controlling the crucible

Sifa

Pawlakos²,

Zhai³

et al. 2018

Proceedings of the Australasian Computer Science Week Multiconference

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“…That is, the top-N prediction may be considered more valuable than the score prediction. Many existing recommendation algorithms are based on top-N prediction, and have great performance [26,27]. We take the top-N approach in this paper.…”

Section: Top-n Recommendationsmentioning

confidence: 99%

Incorporating Multiple Attributes in Social Networks to Enhance the Collaborative Filtering Recommendation Algorithm

Jiang¹,

Xiao²,

Liu³

2016

ijacsa

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Abstract-In view of the existing user similarity calculation principle of recommendation algorithm is single, and recommender system accuracy is not well, we propose a novel social multi-attribute collaborative filtering algorithm (SoMu). We first define the user attraction similarity by users' historical rated behaviors using graph theory, and secondly, define the user interaction similarity by users' social friendship which is based on the social relationship of being followed and following. Then, we combine the user attraction similarity and the user interaction similarity to obtain a multi-attribute comprehensive user similarity model. Finally, realize personalized recommendation according to the comprehensive similarity model. Experimental results on Douban and MovieLens show that the proposed algorithm successfully incorporates multiple attributes in social networks to recommendation algorithm, and improves the accuracy of recommender system with the improved comprehensive similarity computing model.

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Performance of recommender algorithms on top-n recommendation tasks

Cited by 1,134 publications

References 15 publications

Personalised Dynamic Viewer Profiling for Streamed Data

Personalised Dynamic Viewer Profiling for Streamed Data

Controlling the crucible

Incorporating Multiple Attributes in Social Networks to Enhance the Collaborative Filtering Recommendation Algorithm

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