Learning to Rank Features for Recommendation over Multiple Categories

By Xu Chen, Zheng Qin, Yongfeng Zhang, Tao Xu. 2016

162 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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. Yongfeng Zhang
2. Xu Chen
3. Qingyao Ai
4. Zheng Qin
5. W. Bruce Croft
6. Xiangnan He
7. B. Liu
8. Jiaxi Tang

In Information Systems

1. Yongfeng Zhang
2. Xu Chen
3. Xiangnan He
4. Zheng Qin
5. Qingyao Ai
6. Hongyuan Zha
7. W. Bruce Croft
8. Jiaxi Tang

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, background): “2: (a) Learning to rank features for explainable recommendation over multiple categories based on tensor factorization (Chen et al., 2016).”
• ReEL: Review Aware Explanation of Location Recommendation (2018, methodology): “Explanation-based approaches: Chen et al. [11] personalized ranking based tensor factorization model and used phrase-level sentiment analysis across multiple categories.”
• Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis (2018, background): “dynamic user preferences on item features [40] or performing tensor factorization over multiple categories of items [9].”
• Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (2017, methodology): “Di‚erent types of feedbacks describe di‚erent aspects of user preferences – numerical ratings indicate users’ overall aŠitude towards a product; textual reviews are able to express user opinions towards various product features [9, 27, 42]; and product images reveal users’ preferences on di‚erent vi”
• A historical perspective of explainable Artificial Intelligence (2020, methodology): “Tensor factorization is an extension of EFM's, where the cube user-item-features is used to predict ratings with embedded explanations in terms of features (X. Chen, Qin, Zhang, & Xu, 2016).”
• Transparent, Scrutable and Explainable User Models for Personalized Recommendation (2019, background): “Note that this work, along with many others [9, 11, 27, 30, 43, 52], generates explanations with the help of reviews written for items, which may not always be available.”
• Explainable Recommendation via Multi-Task Learning in Opinionated Text Data (2018, background): “tratingly difficult to explain the generated recommendations. The lack of interpretability in factorization-based algorithms has attracted increasing attention in the research community. Zhang et al. [6, 38] combined techniques for phrase-level sentiment analysis with matrix factorization. Abdollahi”
• Adversarial Distillation for Efficient Recommendation with External Knowledge (2018, background): “External knowledge, such as user review or product image, has beenwidely incorporated intomodern recommender systems, because it can not only reveal more comprehensive user/item properties to enhance the recommendation performance, but also provides opportunities to make the recommendations more exp”
• Generate Natural Language Explanations for Recommendation (2021, background): “Since user textual reviews are informative and be‹er reƒect user preferences, a lot of research explored the possibility of incorporating user reviews to improve the recommendation quality [1, 18, 20, 41, 46] and recommendation explainability [2, 3, 8, 16, 27, 38, 45], which helps to enhance the e‚e”
• Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System (2018, methodology): “pervised learning is similar as they both utilize unlabeled data while with different purpose. Transfer Learning for Recommender System Transfer learninghasbeenwidelyusedinthefieldofrecommendersystems[5,12]. These methods mainly focus on how to transfer knowledge (e.g., user rating patterns) from a ”
• Dynamic Explainable Recommendation Based on Neural Attentive Models (2019, methodology): “More specifically, early methods (Zhang et al. 2014; Chen et al. 2016b) mainly based themselves on the combination between matrix factorization (MF) and sentiment analysis (SA).”
• Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation (2019, background): “By discovering such fine-grained user preference, our recommended items can also be explained visually, which is more vivid, convenient, attractive, and comprehensively-efficient as compared with traditional textual explanations [7, 26, 31, 46] for on line users in the fashion domain.”
• Sequential Recommendation with User Memory Networks (2018, unknown): “user textual reviews [2, 12, 33, 34]) for explanations.”
• Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews (2017, methodology): “(1) LRPPM model [7] that we described in Section 5.”