Long-tail Session-based Recommendation

Liu, Siyi; Zheng, Yujia

doi:10.1145/3383313.3412222

Cited by 77 publications

(47 citation statements)

References 19 publications

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“…The analysis done to choose an appropriate idle time relied on the clear user behavior emerging from the clicks, i.e., short, mid and long-term actions. This was partially due to some highly regular patterns, e.g., 24-hour peaks, that may be of particular importance in the VoD domain but may not be present in others. In those cases, it may be more difficult to choose an appropriate idle time and more advanced strategies may be advisable.…”

Section: Limitations and Future Research Directionsmentioning

confidence: 99%

From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain

et al. 2022

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This work presents a comprehensive study, from an industrial perspective, of the process between the collection of raw data, and the generation of next-item recommendation, in the domain of Video-on-Demand (VoD). Most research papers focus their efforts on analyzing recommender systems on already-processed datasets, but they do not face the same challenges that occur naturally in industry, e.g., processing raw interactions logs to create datasets for testing. This paper describes the whole process between data collection and recommendation, including cleaning, processing, feature engineering, session inferring, and all the challenges that a dataset provided by an industrial player in the domain posed. Then, a comparison on the new dataset of several intent-based recommendation techniques in the nextitem recommendation task follows, studying the impact of different factors like the session length, and the number of previous sessions available for a user. The results show that taking advantage of the sequential data available in the dataset benefits recommendation quality, since deep learning algorithms for sessionaware recommendation are consistently the most accurate recommenders. Lastly, a summary of the different challenges in the VoD domain is proposed, discussing on the best algorithmic solutions found, and proposing future research directions to be conducted based on the results obtained.

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Section: Limitations and Future Research Directionsmentioning

confidence: 99%

From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain

et al. 2022

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“…This framework constructs a knowledge graph based on the news entities to improve article embeddings generated by graph neural networks. Liu and Zheng [35] proposed TailNet model which is an SBRS that gives more attention to the long-tail items to make the recommendations more serendipitous. Wu et al [36] introduced an SBRS with graph neural network (SR-GNN).…”

Section: Session-based Recommender Systemsmentioning

confidence: 99%

Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems

Gharahighehi

Vens

2021

SN COMPUT. SCI.

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One of the most important concerns about recommender systems is the filter bubble phenomenon. While recommender systems try to personalize information, they tighten the filter bubble around the users and deprive them of a wide range of content. To overcome this problem, one can diversify the personalized recommendation list. A diversified list usually presents a broader content to the user. Session-based recommender systems are types of recommenders in which only the current session of the user is available, and therefore, they should recommend the next item given the items in the current session. While diversifying conventional recommender systems has been well assessed in the literature, it has gained less attention in session-based recommenders. Diversity and accuracy usually have a negative correlation, i.e., by improving one the other one will be declined. In this study, we propose diversity and accuracy enhancing approaches based on sequential rule mining and session-based k-nearest neighbor methods. Finally, we propose a performance balancing approach that improves both the diversity and accuracy of these session-based recommender systems. We demonstrate the performance of the proposed methods on four music recommender datasets. Keywords Session-based recommenders • Diversity • Session-based k-nearest neighbor • Sequential rule mining • Filter bubble phenomenonThis article is part of the topical collection "Advanced Theories and Algorithms for Next-generation Recommender Systems" guest edited by

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“…Aiming at the long-tail problem caused by the high repetition rate of recommended items in the nearest neighbor In addition, Liu and Zheng [35] proposed a framework for long-tail item recommendation from session data, and it is designed based on a neural network and attention mechanism. Three methods are used to verify coverage, namely, coverage, tail_coverage, and tail.…”

Section: Multiobjective Optimization-based Long-tail Itemmentioning

confidence: 99%

“…Long-Tail Item Recommendation for Sequential Data. In the long-tail item recommendation method, the research results on sequential data recommendation are few, and only Liu and Zheng [35] proposed a long-tail recommendation framework based on session data. If the recommendation method only considered the data in each session to recommend long-tail items to users, the long-tail items will only be related to the short-term preferences of users, ignoring their long-term preferences.…”

Section: 3mentioning

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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Long-tail Session-based Recommendation

Cited by 77 publications

References 19 publications

From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain

From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain

Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems

A Survey of Long‐Tail Item Recommendation Methods

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