About this Research Topic
Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms, and social networks, to provide suggestions that a user will most likely consume or connect, and thus, improving the user experience.
This motivates people in both industry and research organizations to focus on personalization or recommendation algorithms, which has resulted in a plethora of research papers.
While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment. These key factors include but are not limited to: business metric definition and evaluation, recommendation quality control, data and model scalability, model interpretability, model robustness and fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc.
The gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia’s contributions for industrial recommendation systems.
This Research Topic aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue through which academic researchers become aware of the additional factors that may affect the adoption of an algorithm into real production systems, and how well it will perform if deployed. Industrial researchers will also benefit from sharing practical insights, approaches, and frameworks as well.
This new article collection welcomes submissions from researchers in both academia and industry broadly related to recommendation systems, such as novel recommendation models, efficient recommendation algorithms, novel industrial frameworks, etc. We especially welcome submissions on industrial recommendation system infrastructures, models, and algorithms supported by the specific infrastructures, and frameworks or end-to-end systems that have been developed and deployed in real-world production.
Specific topics of interest include but are not limited to:
- Frameworks or end-to-end systems from industry are extremely welcomed
- Scalable Recommender systems
- Personalization, including personalized product recommendation, streaming content recommendation, ads recommendation, news and article recommendation, etc
- New applications related to recommendation systems
- Existing or novel infrastructures for recommendation systems
- Interactive recommendation system with user feedback loop
- Explainability of recommendations
- Fairness, privacy, and security in recommender systems
- Recommendations under multi-objective and constraints
- Reproducibility of models and evaluation metrics
- Unbiased recommendation
- User research studies on real-world recommender systems
- Business impact of recommendation systems
Participants to the "Workshop on Industrial Recommendation Systems" are welcome to submit their contributions to this Research Topic. Workshop papers can be submitted in the form of extended papers: authors are requested to expand it adding 30% of original content in the form of new raw material (experiments, data) or new treatment of old data sets which lead to original discussion and/or conclusions.
Topic editor Jianpeng Xu is employed by Walmart Inc. Topic editor Mohit Sharma is employed by Google Inc.
Topic editor Justin Basilico is employed by Netflix Inc. Topic editor Dawei Yin is employed by Baidu Inc. Topic editor George Karypis is employed by Amazon Inc and is affiliated with the University of Minnesota Twin Cities. Topic editor Philip S. Yu is affiliated with the University of Illinois at Chicago and declares no competing interests with regards to the Research Topic subject.
We acknowledge the funding of some of the manuscripts submitted to this Research Topic by the companies Walmart Inc, Google Inc, Netflix Inc, Baidu Inc, and Amazon Inc. We hereby state publicly that Walmart Inc, Google Inc, Netflix Inc, Baidu Inc, and Amazon Inc have had no editorial input in articles included in this Research Topic, thus ensuring that all aspects of this Research Topic are evaluated objectively, unbiased by any specific policy or opinion of Walmart Inc, Google Inc, Netflix Inc, Baidu Inc, and Amazon Inc.
This motivates people in both industry and research organizations to focus on personalization or recommendation algorithms, which has resulted in a plethora of research papers.
While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment. These key factors include but are not limited to: business metric definition and evaluation, recommendation quality control, data and model scalability, model interpretability, model robustness and fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc.
The gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia’s contributions for industrial recommendation systems.
This Research Topic aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue through which academic researchers become aware of the additional factors that may affect the adoption of an algorithm into real production systems, and how well it will perform if deployed. Industrial researchers will also benefit from sharing practical insights, approaches, and frameworks as well.
This new article collection welcomes submissions from researchers in both academia and industry broadly related to recommendation systems, such as novel recommendation models, efficient recommendation algorithms, novel industrial frameworks, etc. We especially welcome submissions on industrial recommendation system infrastructures, models, and algorithms supported by the specific infrastructures, and frameworks or end-to-end systems that have been developed and deployed in real-world production.
Specific topics of interest include but are not limited to:
- Frameworks or end-to-end systems from industry are extremely welcomed
- Scalable Recommender systems
- Personalization, including personalized product recommendation, streaming content recommendation, ads recommendation, news and article recommendation, etc
- New applications related to recommendation systems
- Existing or novel infrastructures for recommendation systems
- Interactive recommendation system with user feedback loop
- Explainability of recommendations
- Fairness, privacy, and security in recommender systems
- Recommendations under multi-objective and constraints
- Reproducibility of models and evaluation metrics
- Unbiased recommendation
- User research studies on real-world recommender systems
- Business impact of recommendation systems
Participants to the "Workshop on Industrial Recommendation Systems" are welcome to submit their contributions to this Research Topic. Workshop papers can be submitted in the form of extended papers: authors are requested to expand it adding 30% of original content in the form of new raw material (experiments, data) or new treatment of old data sets which lead to original discussion and/or conclusions.
Topic editor Jianpeng Xu is employed by Walmart Inc. Topic editor Mohit Sharma is employed by Google Inc.
Topic editor Justin Basilico is employed by Netflix Inc. Topic editor Dawei Yin is employed by Baidu Inc. Topic editor George Karypis is employed by Amazon Inc and is affiliated with the University of Minnesota Twin Cities. Topic editor Philip S. Yu is affiliated with the University of Illinois at Chicago and declares no competing interests with regards to the Research Topic subject.
We acknowledge the funding of some of the manuscripts submitted to this Research Topic by the companies Walmart Inc, Google Inc, Netflix Inc, Baidu Inc, and Amazon Inc. We hereby state publicly that Walmart Inc, Google Inc, Netflix Inc, Baidu Inc, and Amazon Inc have had no editorial input in articles included in this Research Topic, thus ensuring that all aspects of this Research Topic are evaluated objectively, unbiased by any specific policy or opinion of Walmart Inc, Google Inc, Netflix Inc, Baidu Inc, and Amazon Inc.
Keywords: Recommender Systems, Explainability, Fairness, Privacy, Security, Multi-objective, Reproducibility, Unbiased Recommendation, Business Impact
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
