Personalized Recommenders

• A Contextual-Bandit Approach to Personalized News Article Recommendation
  “This paper addresses the challenge of identifying the most appropriate web-based content at the best time for individual users. Most service vendors acquire and maintain a large amount of content in …
• A Personalized Recommender System based-on Knowledge Graph Embeddings
  “A knowledge graph by means of ontology creates a structured framework for a set of concepts or terms within a specific domain by arranging them in a hierarchical manner, and by using relation descrip…
• A Probabilistic Model for Using Social Networks in Personalized Item Recommendation
  “We first review previous research on using social networks to help recommend items to users. A crucial component of SPF is that it infers the influence that users have with each other. In previous wo…
• Explainable Recommendation with Personalized Review Retrieval and Aspect Learning
  “Recent years have witnessed a growing interest in the development of explainable recommendation models [1, 2]. In general, there are three different kinds of frameworks for explainable recommendation…
• Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
  “News Recommendation (NR) is the process of recommending news articles to users to satisfy their need for information by optimizing the accuracy of predicting relevance between news articles and users…
• LLM-Rec: Personalized Recommendation via Prompting Large Language Models
  The use of large language models in recommender systems has garnered significant attention in recent research. Numerous studies have explored the direct use of LLMs as recommender models. The underlyi…
• Preference Discerning with LLM-Enhanced Generative Retrieval
  Sequential recommendation systems aim to provide personalized recommendations for users based on their interaction history. To achieve this, they often incorporate auxiliary information, such as textu…
• Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
  different recommendation tasks typically require designing task-specific architectures and training objectives. As a result, it is hard to transfer the learned knowledge and representations from one t…
• Review-LLM: Harnessing Large Language Models for Personalized Review Generation
  Product review generation is an important task in recommender systems, which could provide explanation and persuasiveness for the recommendation. Recently, Large Language Models (LLMs, e.g., ChatGPT) …
• Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation
  Large language models (LLMs) can perform recommendation tasks by taking prompts written in natural language as input. Compared to traditional methods such as collaborative filtering, LLM-based recomme…
• The Architectural Implications of Facebook’s DNN-based Personalized Recommendation
  “Deep learning has become a cornerstone in many production scale data center services. As web-based applications continue to expand globally, so does the amount of compute and storage resources devote…
• The persuasive effects of political microtargeting in the age of generative artificial intelligence
  Recent technological advancements, involving generative AI and personality inference from consumed text, can potentially create a highly scalable “manipulation machine” that targets individuals based …