A switching multi-level method for the long tail recommendation problem

Alshammari, Gharbi; Jorro-Aragoneses, Jose L.; Polatidis, Nikolaos; Kapetanakis, Stelios; Pimenidis, Elias; Petridis, Miltos

doi:10.3233/jifs-179331

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…These strategies are still being successfully applied to address various problems in recommender systems. For instance, Rebelo et al [45] have used the cascade strategy to improve the novelty and diversity of recommendations; Alshammari et al [46] have applied the switching strategy to address the problem of long tail recommendations; Hu et al [47] have combined algorithms in a cascading fashion to improve the personalization of recommendations; and Gatzioura et al [48] have implemented a meta-level hybrid recommender to explore metrics such as coherence and diversity in music recommendations.…”

Section: Hybrid Recommendersmentioning

confidence: 99%

New Hybrid Techniques for Business Recommender Systems

2022

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Besides the typical applications of recommender systems in B2C scenarios such as movie or shopping platforms, there is a rising interest in transforming the human-driven advice provided, e.g., in consultancy via the use of recommender systems. We explore the special characteristics of such knowledge-based B2B services and propose a process that allows incorporating recommender systems into them. We suggest and compare several recommender techniques that allow incorporating the necessary contextual knowledge (e.g., company demographics). These techniques are evaluated in isolation on a test set of business intelligence consultancy cases. We then identify the respective strengths of the different techniques and propose a new hybridisation strategy to combine these strengths. Our results show that the hybridisation leads to substantial performance improvement over the individual methods.

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Section: Hybrid Recommendersmentioning

confidence: 99%

New Hybrid Techniques for Business Recommender Systems

2022

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“…Other Long-Tail Item Recommendation Methods. In addition to the above-mentioned long-tail item recommendation methods, researchers have also adopted the ranking method [25], linear-model-based method [27], relevance model-based methods [37], user value-based method [38], and multilevel item similarity calculation method [39].…”

Section: Multiobjective Optimization-based Long-tail Itemmentioning

confidence: 99%

“…Personal values are an important factor affecting users' purchases. Hattori and Takama used the RMRate (Rating Matching Rate) model [39] proposed a long-tail item recommendation framework based on the multilevel similarity method [73] to calculate the similarity between items. The proposed framework includes two components (a collaborative filtering component and content-based component).…”

Section: Multiobjective Optimization-based Long-tail Itemmentioning

confidence: 99%

A Survey of Long‐Tail Item Recommendation Methods

Qin

2021

Wireless Communications and Mobile Computing

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Recommender systems represent a critical field of AI technology applications. The core function of a recommender system is to recommend items of interest to users, but if it is only user history-based (purchasing or browsing data), it can only recommend similar products to a user, which makes the user feel fatigued (creating so-called “Information Cocoons”). Besides, transaction data (purchasing or browsing data) in various fields usually follow Pareto distributions. Accordingly, 20% of products are purchased or viewed a greater number of times (short-head items), while the remaining 80% of products are purchased or viewed less frequently (long-tail items). Using the traditional recommendation method, considering only the accuracy of recommendations, the coverage rate is relatively low, and most of the recommended items are short-head items. The long-tail item recommendation method not only considers the recommendation of short-head items but also considers recommending more long-tail items to users, thus improving the coverage and diversity of the recommendation results. Long-tail item recommendation research has become a frontier issue in recommendation systems in recent years. While the current research paper is still scarce, there have been related research achievements in top-level conferences in the field of computers, such as VLDB and IJCAI. Due to the fact that there is no review literature in this field, to allow readers to better understand the research status of the long-tail item recommendation method, this paper summarizes the progress of the research on long-tail item recommendation methods (from clustering-based, which began in 2008, to deep learning-based methods, which began in 2020) and the future directions associated with this research.

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