Item Recommendation(s) Algorithm

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An Item Recommendation(s) Algorithm is an information filtering algorithm that can be implemented by an item recommendations system to solve an item recommendations task (that maximize item relevance).



References

2024

  • GPT-4
    • Q: Search for the most recent advancements and innovations in item recommendation algorithms. Do not address "systems" (which are implementations) but their algorithms. Make the terms unambigious, e.g. Deep Learning-based Item Recommendation Algorithms.
    • Recent innovations in item recommendation algorithms have focused on the following areas, with each development emphasizing more advanced and personalized approaches:
      • Deep Learning-based Item Recommendation Algorithms: These utilize neural networks like CNN, LSTM, RNN, and AutoEncoder to enhance the prediction of user preferences.
      • Graph Neural Network-based Recommendation Algorithms: Employing models like Spectral CNN, GCN, GraphSAGE, and GAT, these algorithms address the complexity of user-item relationships for more effective recommendations.
      • Context-Aware Recommendation Algorithms: These incorporate contextual information such as time, location, and user mood, leading to more relevant and accurate suggestions.
      • Content-Based Filtering Algorithms: Focused on personal classification based on item features, these algorithms provide personalized recommendations by learning from the attributes of items.
      • Knowledge-Based Recommendation Algorithms: Distinguished by their use of domain knowledge instead of user-item interaction history, these algorithms are particularly effective in complex domains and in overcoming challenges like the cold start problem.

2017

2022

  • (Zhaoin et al., 2022) ⇒ Wayne X. Zhaoin, Zihan Lin, Zhichao Feng, Pengfei Wang, and Ji-Rong Wen. (2022). “A Revisiting Study of Appropriate Offline Evaluation for Top-N Recommendation Algorithms.” ACM Transactions on Information Systems 41, no. 2
    • ABSTRACT: In recommender systems, top-N recommendation is an important task with implicit feedback data. Although the recent success of deep learning largely pushes forward the research on top-N recommendation, there are increasing concerns on appropriate evaluation of recommendation algorithms. It therefore is important to study how recommendation algorithms can be reliably evaluated and thoroughly verified. This work presents a large-scale, systematic study on six important factors from three aspects for evaluating recommender systems. We carefully select 12 top-N recommendation algorithms and eight recommendation datasets. Our experiments are carefully designed and extensively conducted with these algorithms and datasets. In particular, all the experiments in our work are implemented based on an open sourced recommendation library, Recbole [139], which ensures the reproducibility and reliability of our results. Based on the large-scale experiments and detailed analysis, we derive several key findings on the experimental settings for evaluating recommender systems. Our findings show that some settings can lead to substantial or significant differences in performance ranking of the compared algorithms. In response to recent evaluation concerns, we also provide several suggested settings that are specially important for performance comparison.

2000