Recommender Systems Handbook

By Bracha Shapira. 2015

3,172 citations recorded by the source corpus.

Subfields: Information Systems, Recommender Systems

How researchers see this paper

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

In Information Systems

1. Yongfeng Zhang
2. Xiangnan He
3. Xu Chen
4. Julian McAuley
5. Tat-seng Chua
6. Qingyao Ai
7. Xiang Wang
8. Dietmar Jannach

In Recommender Systems

1. Xiangnan He
2. Yongfeng Zhang
3. Julian McAuley
4. Ruining He
5. Xu Chen
6. Harald Steck
7. Wang-Cheng Kang
8. Dietmar Jannach

Citations in the corpus

• From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, methodology): “We start with the ‘standard’ latent-factor recommender system [33], which predicts ratings for user/item pairs ( u, i ) according to Here, α is a global offset, β u and β i are user and item biases (respectively), and γ u and γ i are latent user and item features.”
• Human-Data Interaction: The Human Face of the Data-Driven Society (2014, background): “Recommender systems (Ricci et al., 2010) can provide a useful function, saving us time and effort.”
• Automated Machine Learning: Methods, Systems, Challenges (2019, neutral): “Notable first steps in this direction are applying NAS to language modeling [74], music modeling [48], image restoration [58] and network compression [3]; applications to reinforcement learning, generative adversarial networks, semantic segmentation, or sensor fusion could be further promising futur”
• Self-Attentive Sequential Recommendation (2018, background): “dimensions to represent users’ preferences and items’ properties, and estimate interactions through the inner product between the user and item embeddings [6], [7].”
• Explore-exploit in top-N recommender systems via Gaussian processes (2014, background): “Recommender systems, ranking and relevance: Popular techniques include collaborative filtering, matrix factorization and frequent item set mining [21], as well as learning to rank approaches [2].”
• Towards Conversational Search and Recommendation: System Ask, User Respond (2018, background): “There has been a long track of research efforts for product recommendation in e-commerce [27], and considerable work has been done on product search in e-commerce based on structured knowledge [20].”
• FISM: factored item similarity models for top-N recommender systems (2013, methodology): “Over the years, many algorithms have been developed to address the top-N recommender problem [12].”
• Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles (2015, background): “Examples of the latter can be found in the rich literature on recommender systems (Kantor et al., 2011; Gretzel and Fesenmaier, 2006), or in the attempts to serving personalized ads through behavioral targeting (Stallworth, 2010).”