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On-device recommender systems: A comprehensive survey
Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …
help users identify content of interest from massive amounts of information. Traditional …
Embedding compression in recommender systems: A survey
To alleviate the problem of information explosion, recommender systems are widely
deployed to provide personalized information filtering services. Usually, embedding tables …
deployed to provide personalized information filtering services. Usually, embedding tables …
Bias and debias in recommender system: A survey and future directions
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …
system (RS), most of the papers focus on inventing machine learning models to better fit …
Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences
Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …
recommendation accuracy and explainability. However, existing methods largely assume …
CKAN: Collaborative knowledge-aware attentive network for recommender systems
Since it can effectively address the problem of sparsity and cold start of collaborative
filtering, knowledge graph (KG) is widely studied and employed as side information in the …
filtering, knowledge graph (KG) is widely studied and employed as side information in the …
Neural factorization machines for sparse predictive analytics
Many predictive tasks of web applications need to model categorical variables, such as user
IDs and demographics like genders and occupations. To apply standard machine learning …
IDs and demographics like genders and occupations. To apply standard machine learning …
Neural collaborative filtering
In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the exploration of …
recognition, computer vision and natural language processing. However, the exploration of …
Attentional factorization machines: Learning the weight of feature interactions via attention networks
Factorization Machines (FMs) are a supervised learning approach that enhances the linear
regression model by incorporating the second-order feature interactions. Despite …
regression model by incorporating the second-order feature interactions. Despite …
Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention
Multimedia content is dominating today's Web information. The nature of multimedia user-
item interactions is 1/0 binary implicit feedback (eg, photo likes, video views, song …
item interactions is 1/0 binary implicit feedback (eg, photo likes, video views, song …
NAIS: Neural attentive item similarity model for recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used for building
recommender systems in industrial settings, owing to its interpretability and efficiency in real …
recommender systems in industrial settings, owing to its interpretability and efficiency in real …