Top-N Recommendation Algorithms: A Quest for the State-of-the-Art

Anelli, Vito Walter; Bellogín, Alejandro; Noia, Tommaso Di; Jannach, Dietmar; Pomo, Claudio

doi:10.1145/3503252.3531292

Cited by 21 publications

(11 citation statements)

References 26 publications

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“…Their paper shows several significant differences in performance between user groups scoring high vs. groups scoring low on several personality traits. Vito Walter Anelli et al [17] establish a common understanding of the state-of-the-art for top-n recommendation tasks. The results of the research show that there is no consistent winner across datasets and metrics for the examined top-n recommendation task.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] PCA and Binary K‐Means Clustering Based Collaborative Filtering Recommendation

Peng

Wang

et al. 2023

Journal of Sensors

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Aiming at the problem of similarity calculation error caused by the extremely sparse data in collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on slope one matrix prefilling model, principal component dimension reduction, and binary K -means clustering is proposed in this paper. Firstly, the algorithm uses the slope one model based on item similarity to prefill the original scoring matrix. Secondly, principal component analysis is used to reduce the dimension of the filled matrix, retain the most representative dimension of user characteristics, and remove the dimension with less information. Finally, in order to solve the time-consuming problem of similarity calculation of collaborative filtering algorithm in the case of large-scale system, binary K -means clustering is carried out in the reduced dimension vector space to reduce the search range of the nearest neighbour of the target user. The algorithm ensures the efficiency and accuracy of recommendation while the scale of users is expanded. The experimental results on movielens dataset show that the algorithm proposed in this paper is superior to the traditional collaborative filtering algorithm and the collaborative filtering recommendation algorithm based on PCA (principal component analysis) and binary K -means clustering in recall rate, accuracy rate, average error, and running time.

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Section: Introductionmentioning

confidence: 99%

[Retracted] PCA and Binary K‐Means Clustering Based Collaborative Filtering Recommendation

Peng

Wang

et al. 2023

Journal of Sensors

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“…The Beauty dataset contains product reviews and ratings from Amazon. In line with prior research [1], we pre-processed the dataset to include at least five interactions per user and item (p-core = 5). The Delivery Hero dataset contains anonymous QCommerce sessions for dark store and local shop orders.…”

Section: Methodsmentioning

confidence: 99%

“…Under this light, we can observe a parallel between research in Natural Language Processing (NLP) and sequential recommendation, where novel recommendation models are inspired by NLP models [6]. GRU4Rec [14] adopted the Gated Recurrent Unit (GRU) mechanism from [5], SASRec [21] used the transformer architecture from [37], 1 https://github.com/dh-r/LLM-Sequential-Recommendation and BERT4Rec [35] adopted BERT [7]. The influence of NLP research to sequential recommendation models extends naturally to Large Language Models (LLMs).…”

Section: Introductionmentioning

confidence: 99%

Leveraging Large Language Models for Sequential Recommendation

Harte,

Zorgdrager,

Louridas

et al. 2023

Proceedings of the 17th ACM Conference on Recommender Systems

Self Cite

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Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches. In this work, we explore how large language models (LLMs), which are nowadays introducing disruptive effects in many AI-based applications, can be used to build or improve sequential recommendation approaches. Specifically, we devise and evaluate three approaches to leverage the power of LLMs in different ways. Our results from experiments on two datasets show that initializing the state-of-the-art sequential recommendation model BERT4Rec with embeddings obtained from an LLM improves NDCG by 15-20% compared to the vanilla BERT4Rec model. Furthermore, we find that a simple approach that leverages LLM embeddings for producing recommendations, can provide competitive performance by highlighting semantically related items. We publicly share the code and data of our experiments to ensure reproducibility. 1 CCS Concepts: • Information systems → Recommender systems.

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“…The initial step in this work is to address missing entries in the ratings dataset, as these gaps disrupt the user interest patterns. To accomplish this, a user-user trustcentric approach, as proposed by Awati et al in [33] and J. Bobadila et al [34], is used for filling the missing entries. Once the missing entry-filled dataset is pre-processed, it undergoes vectorization for further processing.…”

Section: Methodsmentioning

confidence: 99%

Hybrid Temporal Dynamics Feature Extraction in Recommendation Systems for Improved Ranking of Items

Awati,

Shirgave

2023

IJRITCC

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In today's retail landscape, shopping malls and e-commerce platforms employ various psychological tactics to influence customer behavior and increase profits. In line with these strategies, this paper introduces an innovative method for recognizing sentiment patterns, with a specific emphasis on the evolving temporal aspects of user interests within Recommendation Systems (RS). The projected method, called Temporal Dynamic Features based User Sentiment Pattern for Recommendation System (TDF-USPRS), aims to enhance the performance of RS by leveraging sentiment trends derived from a user's past preferences. TDF-USPRS utilizes a hybrid model combining Short Time Fourier Transform (STFT) and a layered architecture based on Bidirectional Long Short-Term Memory (BiLSTM) to retrieve temporal dynamics and discern a user's sentiment trend. Through an examination of a user's sequential history of item preferences, TDF-USPRS produces sentiment patterns to offer exceptionally pertinent recommendations, even in cases of sparse datasets. A variety of popular datasets, including as MovieLens, Amazon Rating Beauty, YOOCHOOSE, and CiaoDVD are utilised to assess the suggested technique. The TDF-USPRS model outperforms existing approaches, according to experimental data, resulting in recommendations with greater accuracy and relevance. Comparing the projected model to existing approaches, the projected model displays a 6.5% reduction in RMSE and a 4.5% gain in precision. Specifically, the model achieves an RMSE of 0.7623 and 0.996 on the MovieLens and CiaoDVD datasets, while attaining a precision score of 0.5963 and 0.165 on the YOOCHOOSE and Amazon datasets, respectively.

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Top-N Recommendation Algorithms: A Quest for the State-of-the-Art

Cited by 21 publications

References 26 publications

[Retracted] PCA and Binary K‐Means Clustering Based Collaborative Filtering Recommendation

[Retracted] PCA and Binary K‐Means Clustering Based Collaborative Filtering Recommendation

Leveraging Large Language Models for Sequential Recommendation

Hybrid Temporal Dynamics Feature Extraction in Recommendation Systems for Improved Ranking of Items

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