Content-based recommendations with Poisson factorization

By Prem Gopalan, Laurent Charlin, David M. Blei. 2014

194 citations recorded by the source corpus.

Subfields: Information Retrieval & Search

How researchers see this paper

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

In Information Retrieval & Search

1. Yue Shi
2. Ting-Li Chen
3. Yizhou Sun
4. Liangjie Hong
5. Dawen Liang
6. D. Ellis
7. Tomi Poutanen
8. Maksims Volkovs

Citations in the corpus

• On Sampling Strategies for Neural Network-based Collaborative Filtering (2017, methodology): “To address the issue and improve performance, hybrid methods are proposed to incorporate side information [5, 7, 25, 28, 38], as well as content information [4, 11, 31, 32].”
• A Neural Autoregressive Approach to Collaborative Filtering (2016, positive): “Recently, Poisson Matrix Factorization (Gopalan et al., 2014b;a; 2013) was proposed, replacing Gaussian assumption of PMF by Poisson distribution.”
• Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016, background): “RNNs have a number of noteworthy characteristics: (1) they are sensitive to word order, (2) they do not require hand-engineered features, (3) it is easy to leverage large unlabeled datasets, by pretraining the RNN parameters with unsupervised language modeling objectives [21], (4) RNN computation…”
• Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration (2017, methodology): “TF-IDF feature or naive Bayes and unsupervised learning objective [15, 43], e.”
• DropoutNet: Addressing Cold Start in Recommender Systems (2017, methodology): “Collaborative topic Poisson factorization (CTPF) [8] uses a similar interpolation architecture but replaces both LDA and WMF components with Poisson factorization [9].”
• Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems (2018, background): “Whenanexternaltextualdatasource is available (e.g. product reviews [36] or news articles [15]), topic modelling can be used to discover the themes present in the text [9, 15, 31] which can then be combined with the rating predictions to provide an interpretation of the latent factors.”
• Joint Text Embedding for Personalized Content-based Recommendation (2017, methodology): “Hybrid methods can improve so-called “cold-start” issue by incorporating side information [5, 22, 27], or item content information [8, 30, 31].”
• Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2024, unknown): “Early works in content-based recommendation (Mooney and Roy, 2000; Gopalan et al., 2014) extracted keywords and learned their collaborative relations between users and items (Ren-dle, 2010; Wang and Blei, 2011; Chen et al., 2012; Wang et al., 2015).”
• Modeling User Exposure in Recommendation (2015, result): “We compare to collaborative topic regression (CTR), a state-of-the-art method for recommending scientific papers [30] combining both LDA and WMF. 9 We did not compare with the more recent and scalable collaborative topic Poisson factorization (CTPF) [7] since the resulting performance differences may ”
• Content-Aware Collaborative Music Recommendation Using Pre-trained Neural Networks (2015, methodology): “We set the confidence c ui in the same way as in Section 2.1.”
• Scalable Recommendation with Hierarchical Poisson Factorization (2015, methodology): “One extension is a combined model of article text and reader preferences [14].”
• Supervised Word Mover's Distance (2016, background): “Document distances are a key component of many text retrieval tasks such as web-search ranking [24], book recommendation [16], and news categorization [25].”