Predictability of diffusion-based recommender systems

Zhang, Peng; Xue, Liutang; Zeng, An

doi:10.1016/j.knosys.2019.104921

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…Meanwhile, Jarv roughly measures the predictability of the recommender system by counting the number of first-time samples in the test set [33]. As for the diffusion-based method, Zhang et al pointed out that if the connection could not be established through diffusion within a certain number of steps, it would be impossible to make further correct recommendations [34]. Based on this analysis, the accuracy limits of the method is further deduced.…”

Section: Predictability Of Recommender Systemsmentioning

confidence: 99%

Limits of Predictability in Top-N Recommendation

Xu¹,

Yu²,

Zhang³

et al. 2023

Preprint

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Top-N recommendation aims to recommend each consumer a small set of N items from a large collection of items, and its accuracy is one of the most common indexes to evaluate the performance of a recommendation system. While a large number of algorithms are proposed to push the Top-N accuracy by learning the user preference from their history purchase data, a predictability question is naturally raised -whether there is an upper limit of such Top-N accuracy. This work investigates such predictability by studying the degree of regularity from a specific set of user behavior data. Quantifying the predictability of Top-N recommendations requires simultaneously quantifying the limits on the accuracy of the N behaviors with the highest probability. This greatly increases the difficulty of the problem. To achieve this, we firstly excavate the associations among N behaviors with the highest probability and describe the user behavior distribution based on the information theory. Then, we adopt the Fano inequality to scale and obtain the Top-N predictability. Extensive experiments are conducted on the real-world data where significant improvements are observed compared to the state-of-the-art methods. We have not only completed the predictability calculation for N targets but also obtained predictability that is much closer to the true value than existing methods. We expect our results to assist these research areas where the quantitative requirement of Top-N predictability is required.

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Section: Predictability Of Recommender Systemsmentioning

confidence: 99%

Limits of Predictability in Top-N Recommendation

Xu¹,

Yu²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The result is mainly caused by extreme sparse data set. This is because the coverage of initial resource of mass diffusion is limited in sparse network [39]. By the evolution model and ARL, diverse items are added into evolving network and the link is rewired, which enable resource to cover more items and further could predict items in the probe set.…”

Section: Deliciousmentioning

confidence: 99%

“…The enhancement of longterm performance for n=30 is not obvious in Amazon compared with other datasets, especially for the Gini. This is because most users selected less items and a little number of users bought more niche items in sparse dataset [39]. In such condition, the improvement of Gini is limited by breaking-rewiring process of link.…”

Section: Amazonmentioning

confidence: 99%

Enhancing the long-term performance of recommender system

Xue

Zhang

Zeng

2019

Physica A: Statistical Mechanics and its Applications

Self Cite

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Recommender system is a critically important tool in online commercial system and provide users with personalized recommendation on items. So far, numerous recommendation algorithms have been made to further improve the recommendation performance in a single-step recommendation, while the long-term recommendation performance is neglected. In this paper, we proposed an approach called Adjustment of Recommendation List (ARL) to enhance the long-term recommendation accuracy. In order to observe the long-term accuracy, we developed an evolution model of network to simulate the interaction between the recommender system and user's behaviour. The result shows that not only long-term recommendation accuracy can be enhanced significantly but the diversity of item in online system maintains healthy. Notably, an optimal parameter n * of ARL existed in long-term recommendation, indicating that there is a trade-off between keeping diversity of item and user's preference to maximize the long-term recommendation accuracy. Finally, we confirmed that the optimal parameter n * is stable during evolving network, which reveals the robustness of ARL method.

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Quantifying predictability of sequential recommendation via logical constraints

Yu²,

Li³

et al. 2022

Front. Comput. Sci.

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Predictability of diffusion-based recommender systems

Cited by 5 publications

References 45 publications

Limits of Predictability in Top-N Recommendation

Limits of Predictability in Top-N Recommendation

Enhancing the long-term performance of recommender system

Quantifying predictability of sequential recommendation via logical constraints

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