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Recommender system

Known as: Recommendation search engines, IPTV Recommendations, TV Recommender system 
Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information… Expand
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2018
Review
2018
Neural Attentional Rating Regression with Review-level Explanations
Reviews information is dominant for users to make online purchasing decisions in e-commerces. However, the usefulness of reviews… Expand
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Review
2018
Review
2018
The use of machine learning algorithms in recommender systems: A systematic review
Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been… Expand
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Review
2018
Review
2018
Sequence-Aware Recommender Systems
Recommender systems are one of the most successful applications of data mining and machine-learning technology in practice… Expand
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Review
2018
Review
2018
Multi-Pointer Co-Attention Networks for Recommendation
Many recent state-of-the-art recommender systems such as D-ATT, TransNet and DeepCoNN exploit reviews for representation learning… Expand
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Review
2018
Review
2018
Sentiment Analysis for Software Engineering: How Far Can We Go?
Sentiment analysis has been applied to various software engineering (SE) tasks, such as evaluating app reviews or analyzing… Expand
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Review
2017
Review
2017
A Survey on Dialogue Systems: Recent Advances and New Frontiers
Dialogue systems have attracted more and more attention. Recent advances on dialogue systems are overwhelmingly contributed by… Expand
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Review
2017
Review
2017
Construct Validation in Social and Personality Research: Current Practice and Recommendations
The verity of results about a psychological construct hinges on the validity of its measurement, making construct validation a… Expand
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Review
2017
Review
2017
Deep Multimodal Learning: A Survey on Recent Advances and Trends
The success of deep learning has been a catalyst to solving increasingly complex machine-learning problems, which often involve… Expand
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Review
2017
Review
2017
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources
The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user… Expand
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Review
2017
Review
2017
Diversity in recommender systems - A survey
Diversification has become one of the leading topics of recommender system research not only as a way to solve the over-fitting… Expand
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