Recommender system application developments: A survey

By Jie Lu, Dianshuang Wu, Mingsong Mao, Wei Wang, Guangquan Zhang. 2015

1,587 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Information Systems, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Sarasi Lalithsena
2. Tor Lattimore
3. Naomie Binti Salim
4. Aminu Da’u
5. Pushmeet Kohli
6. Jie Lu
7. András György
8. Ji-rong Wen

In Electrical and Electronic Engineering

1. Sarasi Lalithsena
2. Aminu Da’u
3. Pushmeet Kohli
4. Kun Zhou
5. András György
6. Ji-rong Wen
7. Naomie Binti Salim
8. Hui Yu

In Information Systems

1. Ji-rong Wen
2. Wayne Xin Zhao
3. Kun Zhou
4. Sarasi Lalithsena
5. Naomie Binti Salim
6. Aminu Da’u
7. Pushmeet Kohli
8. Jie Lu

In Evaluation & Benchmarks

1. Sarasi Lalithsena
2. Aminu Da’u
3. Pushmeet Kohli
4. Jie Lu
5. Kun Zhou
6. András György
7. Ji-rong Wen
8. Naomie Binti Salim

Citations in the corpus

• Degenerate Feedback Loops in Recommender Systems (2019, background): “Introduction Recommender systems are increasingly used to provide users with personalized product and information offerings (Ben Schafer, Konstan, and Riedl, 2001; Lu et al., 2015; Covington, Adams, and Sargin, 2016).”
• Filter-enhanced MLP is All You Need for Sequential Recommendation (2022, methodology): “Recommender systems [32, 67, 69, 71] have been widely deployed in online platforms (e.”
• Domain-specific Knowledge Extraction from the Web of Data (2018, methodology): “While content-based approaches recommend the items based on the properties of the items to be recommended, collaborative filtering approaches recommend the items based on the preferences of similar users [Lu et al. 2015].”
• Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “CB filtering approaches usually provide obvious recommendations since they make use of content description (Lu et al. 2015).”
• Artificial intelligence in recommender systems (2020, methodology): “Ref-erencing the classical taxonomies of previous research [4–6], recommendation techniques fall into three categories: content-based, collaborative filtering (CF)-based and knowledge-based approaches.”
• Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, methodology): “In Lu et al. (2015) review of recommender systems, they clustered their applications into eight categories and then summarized the related recommendation techniques used in each category.”