What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
His primary areas of investigation include Recommender system, Knowledge management, Collaborative filtering, Human–computer interaction and Preference elicitation.
His Recommender system research is multidisciplinary, incorporating elements of Quality and Web application.
His research integrates issues of User experience design and Cognitive effort in his study of Knowledge management.
Li Chen focuses mostly in the field of Collaborative filtering, narrowing it down to topics relating to Artificial intelligence and, in certain cases, Friendship and Data mining.
The concepts of his Human–computer interaction study are interwoven with issues in Decision support system and World Wide Web.
His Information retrieval research incorporates themes from Sentiment analysis and Leverage.
His most cited work include:
- A user-centric evaluation framework for recommender systems (431 citations)
- Temporal recommendation on graphs via long- and short-term preference fusion (291 citations)
- News impact on stock price return via sentiment analysis (220 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Recommender system, Human–computer interaction, World Wide Web, Information retrieval and Artificial intelligence.
In the subject of general Recommender system, his work in Collaborative filtering is often linked to Preference elicitation, thereby combining diverse domains of study.
His studies deal with areas such as Decision support system and Task as well as Human–computer interaction.
His biological study spans a wide range of topics, including Sentiment analysis, Leverage and User profile.
His research in Sentiment analysis intersects with topics in Quality and Feature.
In his research on the topic of Artificial intelligence, Hidden Markov model, Data mining and Identification is strongly related with Machine learning.
He most often published in these fields:
- Recommender system (62.02%)
- Human–computer interaction (24.03%)
- World Wide Web (21.71%)
What were the highlights of his more recent work (between 2017-2021)?
- Recommender system (62.02%)
- Human–computer interaction (24.03%)
- Artificial intelligence (16.28%)
In recent papers he was focusing on the following fields of study:
His main research concerns Recommender system, Human–computer interaction, Artificial intelligence, Machine learning and Collaborative filtering.
Li Chen undertakes interdisciplinary study in the fields of Recommender system and Code through his research.
His work on User experience design as part of general Human–computer interaction study is frequently connected to Serendipity, User feedback and Psychological Models, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Artificial intelligence study incorporates themes from Residual and Web browser.
His work in Machine learning addresses issues such as Variety, which are connected to fields such as Gibbs sampling, Hidden Markov model, Mobile apps and Data mining.
His work carried out in the field of Collaborative filtering brings together such families of science as Transfer of learning, Web service and Theoretical computer science.
Between 2017 and 2021, his most popular works were:
- A Survey on Conversational Recommender Systems. (17 citations)
- How Serendipity Improves User Satisfaction with Recommendations? A Large-Scale User Evaluation (17 citations)
- Personalizing recommendation diversity based on user personality (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Recommender system, Collaborative filtering, Data science, Human–computer interaction and Preference learning are his primary areas of study.
The Recommender system study combines topics in areas such as Curiosity and Chatbot, Artificial intelligence.
His Artificial intelligence research incorporates themes from Quality and Machine learning.
Li Chen frequently studies issues relating to Big Five personality traits and Collaborative filtering.
In his study, Personality, Categorization and Ranking is inextricably linked to Set, which falls within the broad field of Data science.
His research brings together the fields of Taxonomy and Human–computer interaction.
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