Recommender Systems (General)

• A Survey on Large Language Models for Recommendation
  LikangWu , Zhi Zheng , Zhaopeng Qiu , Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, Enhong Chen University of Science and Technology of China, Career Scie…
• Cumulated Gain-Based Evaluation of IR Techniques
  “Modern large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified …
• Curse of “Low” Dimensionality in Recommender Systems
  “Beyond accuracy, there are a variety of aspects to the quality of recommender systems, such as diversity, fairness, and robustness. We argue that many of the prevalent problems in recommender systems…
• Fast and Slow Learning From Reviews
  “This paper develops a model of Bayesian learning from online reviews, and investigates the conditions for asymptotic learning of the quality of a product and the speed of learning under different rat…
• I like it... I like it not: Evaluating User Ratings Noise in Recommender Systems
  “A common approach to handle digital information overload is to offer users a personalized access to information. Recommender Systems (RS), for instance, automatically suggest new content that should …
• LLMs as Architects and Critics for Multi-Source Opinion Summarization
  benchmark dataset for evaluating multi-source opinion summaries across 7 key dimensions: fluency, coherence, relevance, faithfulness, aspect coverage, sentiment consistency, specificity. Our results d…
• Measuring the Value of Social Dynamics in Online Product Ratings Forums
  “The common underlying assumption of studies which investigate the impact of consumer reviews on product sales is that posted product ratings reflect the customers’ experience with the product, indepe…
• On Information Distortions in Online Ratings
  “If consumers were perfectly homogeneous when writing a review, they would implicitly reveal the quality of the object. Heterogeneity of the consumers creates a problem both ex ante and ex post. Ex an…
• Posting versus Lurking: Communicating in a Multiple Audience Context
  “It is proposed that posters will be influenced by another’s opinion only when it is negative. Negative evaluators are seen as more intelligent, competent, and expert than positive evaluators (Amabile…
• Recommendation systems and convergence of online reviews: The type of product network matters!
  “The Internet has given word-of-mouth (WOM) a new significance by allowing individuals to express their opinions and thoughts to a global audience, and so, it is an essential aspect of e-commerce [3].…
• Reconciling the accuracy-diversity trade-off in recommendations
  “In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items representing a range of c…
• Self Selection and Information Role of Online Product Reviews
  “Online product reviews may be subject to self-selection biases that impact consumer purchase behavior, online ratings' time series, and consumer surplus. This occurs if early buyers hold different pr…
• Why Do People Rate? Theory and Evidence on Online Ratings
  “The average review score can vary considerably for products that have otherwise similar characteristics, and may be the only insight consumers have into a product's unobservable qualities before they…