Mining product textual data for recommendation explanations

Mining product textual data for recommendation explanations

Recommendation explanations help to make sense of recommendations, increasing the likelihood of adoption. Here, we are interested in mining product textual data, an unstructured data type, coming from manufacturers, sellers, or consumers, appearing in many places including title, summary, descriptio...

Saved in:

書目詳細資料
主要作者:	LE TRUNG HOANG
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2022
主題:	Recommender Systems Recommendation Explanations Aspect-Level Sentiment Review-Level Explanation Question-Level Explanation Evaluative Recommendation Explanation Comparative Recommendation Explanations Review Selection Review Sets Selection Databases and Information Systems
在線閱讀:	https://ink.library.smu.edu.sg/etd_coll/450 https://ink.library.smu.edu.sg/context/etd_coll/article/1448/viewcontent/GPIS_AY2017_PhD_LE_Trung_Hoang.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Singapore Management University
語言:	English

相似書籍

Question-attentive review-level explanation for neural rating regression
由: LE, Trung Hoang, et al.
出版: (2024)

Tagcloud-based explanation with feedback for recommender systems
由: Chen, W., et al.
出版: (2014)

Aspect-guided syntax graph learning for explainable recommendation
由: Hu, Yidan, et al.
出版: (2023)

Explanation guided contrastive learning for sequential recommendation
由: WANG, Lei, et al.
出版: (2022)

Multi modal personalized explanation generation
由: Marantika, Winda Kirana
出版: (2024)

Sequential recommendation: From representation learning to reasoning
由: WANG, Lei
出版: (2024)

Beyond persuasion : towards conversational recommender system with credible explanations
由: QIN, Peixin, et al.
出版: (2024)

Counterfactual explanations on the changes in foreign exchange market
由: Sng, Rhys Yi
出版: (2024)

How to ConQueR why-not questions
由: Tran, Q.T., et al.
出版: (2013)

IMPROVING USABILITY OF MACHINE LEARNING MODEL EXPLANATIONS: EXPERIMENTS WITH VISUAL AND VERBAL COGNITIVE LOAD
由: ASHRAF MOHAMMED ABDUL
出版: (2022)

LAYERED EXPLANATIONS - INTERPRETING NEURAL NETWORKS WITH NUMERICAL INFLUENCE MEASURES
由: HO XUAN VINH
出版: (2019)

ANDEA: anomaly and novelty detection, explanation, and accommodation
由: PANG, Guansong, et al.
出版: (2022)

Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)
由: PANG, Guansong, et al.
出版: (2021)

Reconsidering the role of inference to the best explanation in the epistemology of testimony
由: Gelfert, A.
出版: (2014)

Enhancing explanations in knowledge-based systems with hypertext
由: Mao, J., et al.
出版: (2013)

Counterfactual explanations for forex prediction using deep learning methods
由: Vinod, Vinay Krishnaa
出版: (2024)

Is self-explanation better than explaining to a fictitious student when learning from video lectures?
由: Pi, Zhongling, et al.
出版: (2022)

The skyline of counterfactual explanations for machine learning decision models
由: Wang, Yongjie, et al.
出版: (2022)

Using explanation structures to speed up local-search-based planning
由: TIAN ZHENGMIAO
出版: (2012)

The Role of Power Distance and Explanation Facility in Online Bargaining Utilizing Software Agents
由: Lim, J.
出版: (2013)

Consumer-based decision aid that explains which to buy: Decision confirmation or overconfidence bias?
由: Tan, W.-K., et al.
出版: (2013)

How to interpret the helpfulness of online product reviews: Bridging the needs between customers and designers
由: Jin, J., et al.
出版: (2014)

What Drives ICT Global Adoption? Analysis and Research Directions
由: WEBER, David M., et al.
出版: (2011)

Can we trust your explanations? Sanity checks for interpreters in android malware analysis
由: FAN, Min, et al.
出版: (2021)

An Examination of Affect and its Relationship with Learning among Students using SimStudent
由: Banawan, Michelle P, et al.
出版: (2014)

Demystifying faulty code: Step-by-step reasoning for explainable fault localization
由: WIDYASARI, Ratnadira, et al.
出版: (2024)

MODELING DYNAMIC ASPECTS OF CONTEXT-AWARE RECOMMENDER SYSTEMS
由: THILINA MADUSANKA THANTHRIWATTA
出版: (2021)

Interactive Path Reasoning on Graph for Conversational Recommendation
由: Wenqiang Lei, et al.
出版: (2020)

Understanding students’ use of code-switching in a learning by teaching technology
由: Yarzebinski, Evelyn, et al.
出版: (2015)

Using adjective features from user reviews to generate higher quality and explainable recommendations
由: Xu, X., et al.
出版: (2013)

Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
由: Wenqiang Lei, et al.
出版: (2020)

Economic forecasting in Singapore: The Covid-19 experience
由: CHOW-TAN, Hwee Kwan, et al.
出版: (2023)

EXPLAINING AND IMPROVING DEEP NEURAL NETWORKS VIA CONCEPT-BASED EXPLANATIONS
由: SANDAREKA KUMUDU KUMARI WICKRAMANAYAKE
出版: (2022)

Performance evaluation of aggregation-based group recommender systems for ephemeral groups
由: CEH-VARELA, Edgar, et al.
出版: (2022)

Answering why-not and why questions on reverse top-k queries
由: LIU, Qing, et al.
出版: (2016)

Recommending APIs for software evolution
由: THUNG, Ferdian
出版: (2018)

Toward explainable deep anomaly detection
由: PANG, Guansong, et al.
出版: (2021)

Using critic reviews to boost new item recommendation
由: Xu, X., et al.
出版: (2016)

REVEALING PRIVACY RISKS IN PRIVACY-PRESERVING MACHINE LEARNING: A STUDY OF DATA INFERENCE ATTACKS
由: LUO XINJIAN
出版: (2024)

Conversational Recommendation: Formulation, Methods, and Evaluation
由: Wenqiang Lei, et al.
出版: (2020)