Cross-Domain Recommender Systems

By Iván Cantador, Ignacio Fernández-Tobías, Shlomo Berkovsky, Paolo Cremonesi. Recommender Systems Handbook, 2015

326 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/285b763f72361b5d76ba2eeec064bfffe65345b1

How researchers see this paper

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

In Artificial Intelligence

1. Qiang Yang
2. Yu Zhang
3. Yaochu Jin
4. Jie Lu
5. Qian Zhang
6. Guangneng Hu
7. Hamed Zamani
8. Patrick Gallinari

In Information Systems

1. Jie Lu
2. Yaochu Jin
3. Qian Zhang
4. Qiang Yang
5. Guangneng Hu
6. Yu Zhang
7. Hamed Zamani
8. Markus Schedl

In Management Science and Operations Research

1. Qiang Yang
2. Markus Schedl
3. Jie Lu
4. Patrick Gallinari
5. Jun Guo
6. Ching-Wei Chen
7. Qian Zhang
8. Yaochu Jin

Citations in the corpus

• Artificial intelligence in recommender systems (2020, methodology): “Some methods connect two domains through auxiliary information other than preference data [20], while CDRS based on preference data can be strategically designed according to the overlap of users and items, theformthedatatakes,orthetasksthesystemneedstohandle [109].”
• Cross-domain Recommendation Without Sharing User-relevant Data (2019, unknown): “Specifically, our problem is a typical system-level cross-domain recommendation task, where same items are shared across domains, according to the definition in two surveys [2, 21].”
• Hybrid recommender systems: A systematic literature review (2017, background): “Cross-domain RSs are an emerging research topic [41, 42].”
• CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (2018, background): “Cross-domain recommendation [2] is an effective technique to alleviate sparse issue.”
• Current challenges and visions in music recommender systems research (2017, background): “For amore detailed literature review of cross-domain recommendation, we refer to [29,68,102].”
• Cross-Domain Recommendation via Cluster-Level Latent Factor Model (2013, background): “[14] considered to model the classical similarity relationships as a direct graph and explore all possible paths connecting users or items in order to find new cross-domain relationships.”
• Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text (2019, background): “Cross-domain recommendation [3] is an effective technique to alleviate sparse issue where transfer learning (including multitask learning) [4, 38, 59] is a class of underlying methods.”