733 citations · 3.6k across the 42 of their papers we have counts for
7 papers · 1 filter
Attentive Aspect Modeling for Review-aware Recommendation
Xinyu Guan, Zhiyong Cheng, Xiangnan He +4
In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user…
Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
Liu Yang, Minghui Qiu, Chen Qu +5
Intelligent personal assistant systems with either text-based or voice-based conversational interfaces are becoming increasingly popular around the world. Retrieval-based conversat…
Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
Qingyao Ai, Vahid Azizi, Xu Chen +1
Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms - especially collaborative filtering (CF)-…
Analyzing and Characterizing User Intent in Information-seeking Conversations
Chen Qu, Liu Yang, W. Bruce Croft +3
Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems. In this paper, we introduce a new…
Explainable Recommendation: A Survey and New Perspectives
Yongfeng Zhang, Xu Chen
Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc o…
Learning over Knowledge-Base Embeddings for Recommendation
Yongfeng Zhang, Qingyao Ai, Xu Chen +1
State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured inf…
Visually Explainable Recommendation
Xu Chen, Yongfeng Zhang, Hongteng Xu +3
Images account for a significant part of user decisions in many application scenarios, such as product images in e-commerce, or user image posts in social networks. It is intuitive…