Item Recommendation(s) Algorithm

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).

• Context:
  • It can (typically) need to account for a Cold-Start Problem.
  • It can utilize a User Profile of the Agent, including past interactions with items, to personalize recommendations.
  • It can range from Data-Driven Item Recommendation Algorithms that leverage large datasets to being a Heuristic Item Recommendation Algorithms based on set rules.
  • It can range from being a Personalized Item Recommendation Algorithms, which tailor suggestions to individual users, to being a Non-Personalized Item Recommendation Algorithms that provide general recommendations.
  • It can range from being a Contextual Item Recommendation Algorithms that consider the current context of the user, to being Non-Contextual Item Recommendation Algorithms that do not.
  • It typically addresses the Cold-Start Problem where limited data is available about new users or items.
  • …
• Example(s):
  • An Item Type-Specific Recommendation Algorithms, such as:
    • A Ad Recommendation Algorithm, to support Ad Recommendations.
    • A Movie Recommendation Algorithm, to support Movie Recommendations.
    • A Social Bookmarking Recommender Algorithm, recommending Articles or Bookmarks based on User Preferences and interactions, as explored by Bogers, 2009.
  • A Cross-Selling Recommender Algorithm, that focuses on Cross-Selling Tasks.
  • A Deep Learning-Based Recommendation Algorithm that uses DNN Methods, such as: CNN, LSTM, RNN, and AutoEncoders.
  • A Graph Neural Network-Based Recommendation Algorithms, that use User-Item Relationships, such as: Spectral CNN, GCN, GraphSAGE, and GAT.
  • A Context-Aware Recommendation Algorithm, that incorporates contextual information such as time, location, and User Mood.
  • A Content-Aware Recommendation Algorithm, that focuses on analyzing and utilizing the content features of items, such as text descriptions, categories, and tags, to make personalized recommendations.
  • A Knowledge-Based Recommendation Algorithm: 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.
  • A Collaborative Filtering-Based Recommendation Algorithm, such as an MF-Based Collaborative Algorithm.
  • An In-Context Learning (ICL)-Based Recommendation Algorithm, that injects domain-specific knowledge,
  • …
• Counter-Example(s):
  • A Spam Filtering Algorithm, which is designed to identify and filter out unwanted emails.
  • A Search Engine Algorithm, which ranks web pages based on relevance to a search query rather than recommending items.
• See: Collaborative Filtering Algorithm.

---

---

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

• (Vartak et al., 2017) ⇒ Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, and Hugo Larochelle. (2017). “A Meta-Learning Perspective on Cold-Start Recommendations for Items.” In: Proceedings of Advances in Neural Information Processing Systems 30 (NIPS-2017).
  • QUOTE: Matrix factorization (MF) is one of the most popular techniques for product recommendation, but is known to suffer from serious cold-start problems.

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

• (Sarwar et al., 2000) ⇒ Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. (2000). “Analysis of Recommendation Algorithms for E-commerce.” In: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 158-167.

---