Papers Movie Recommendation
“Movie Recommendation” 태그가 달린 논문 124편 · 필터 해제
Beyond Models: Reflections on Engineering AI-enabled Systems in a Project-Based Course
Teaching Software Engineering for AI-enabled systems entails addressing the integration of AI components within full-scale software architectures under realistic constraints. While machine learning courses emphasize mode…
Movie RecommendationEvaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation
The growing ubiquity of Extended Reality (XR) is driving Conversational Recommendation Systems (CRS) toward visually immersive experiences. We formalize this paradigm as Immersive CRS (ICRS), where recommended items are …
Recommendation SystemsMovie RecommendationUtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Tasks
The success of a Large Language Model (LLM) task depends heavily on its prompt. Most use-cases specify prompts using natural language, which is inherently ambiguous when multiple objectives must be simultaneously satisfi…
Movie RecommendationDiagnosing LLM-based Rerankers in Cold-Start Recommender Systems: Coverage, Exposure and Practical Mitigations
Large language models (LLMs) and cross-encoder rerankers have gained attention for improving recommender systems, particularly in cold-start scenarios where user interaction history is limited. However, practical deploym…
Movie RecommendationAdaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation
A core research question in recommender systems is to propose batches of highly relevant and diverse items, that is, items personalized to the user's preferences, but which also might get the user out of their comfort zo…
Movie RecommendationPoint ProcessesExplainRec: Towards Explainable Multi-Modal Zero-Shot Recommendation with Preference Attribution and Large Language Models
Recent advances in Large Language Models (LLMs) have opened new possibilities for recommendation systems, though current approaches such as TALLRec face challenges in explainability and cold-start scenarios. We present E…
Recommendation SystemsMovie RecommendationTransfer LearningScaling Homomorphic Applications in Deployment
In this endeavor, a proof-of-concept homomorphic application is developed to determine the production readiness of encryption ecosystems. A movie recommendation app is implemented for this purpose and productionized thro…
Reinforcement LearningMovie RecommendationPersonalized Recommendations via Active Utility-based Pairwise Sampling
Recommender systems play a critical role in enhancing user experience by providing personalized suggestions based on user preferences. Traditional approaches often rely on explicit numerical ratings or assume access to f…
Movie RecommendationParameter-Efficient Single Collaborative Branch for Recommendation
Recommender Systems (RS) often rely on representations of users and items in a joint embedding space and on a similarity metric to compute relevance scores. In modern RS, the modules to obtain user and item representatio…
Representation LearningMovie RecommendationProposing a Semantic Movie Recommendation System Enhanced by ChatGPT's NLP Results
The importance of recommender systems on the web has grown, especially in the movie industry, with a vast selection of options to watch. To assist users in traversing available items and finding relevant results, recomme…
Movie RecommendationDUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendation
The modern recommender systems are facing an increasing challenge of modelling and predicting the dynamic and context-rich user preferences. Traditional collaborative filtering and content-based methods often struggle to…
Collaborative FilteringMovie RecommendationA Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms
Traditional recommendation algorithms are not designed to provide personalized recommendations based on user preferences provided through text, e.g., "I enjoy light-hearted comedies with a lot of humor". Large Language M…
Movie RecommendationRecommendation SystemsLumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Movie Recommendation
Conversational recommender systems (CRSs) often suffer from an extreme long-tail distribution of dialogue data, causing a strong bias toward head-frequency blockbusters that sacrifices diversity and exacerbates the cold-…
DiversityFairnessInformativenessMovie Recommendation+1Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations
Recommender systems play a vital role in helping users discover content in streaming services, but their effectiveness depends on users understanding why items are recommended. In this study, explanations were based sole…
Movie RecommendationRecommendation SystemsMulti-Selection for Recommendation Systems
We present the construction of a multi-selection model to answer differentially private queries in the context of recommendation systems. The server sends back multiple recommendations and a ``local model'' to the user, …
Movie RecommendationRecommendation SystemsMovie Recommendation using Web Crawling
In today's digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real time data from popular movie websites …
Collaborative FilteringMovie RecommendationLarge Language Models as Narrative-Driven Recommenders
Narrative-driven recommenders aim to provide personalized suggestions for user requests expressed in free-form text such as "I want to watch a thriller with a mind-bending story, like Shutter Island." Although large lang…
Movie RecommendationNatural Language QueriesRecommendation SystemsContextual Bandits with Arm Request Costs and Delays
We introduce a novel extension of the contextual bandit problem, where new sets of arms can be requested with stochastic time delays and associated costs. In this setting, the learner can select multiple arms from a deci…
Movie RecommendationMulti-Armed BanditsBayesian Estimation and Tuning-Free Rank Detection for Probability Mass Function Tensors
Obtaining a reliable estimate of the joint probability mass function (PMF) of a set of random variables from observed data is a significant objective in statistical signal processing and machine learning. Modelling the j…
Computational EfficiencyMovie RecommendationVariational InferenceDucho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation
In specific domains like fashion, music, and movie recommendation, the multi-faceted features characterizing products and services may influence each customer on online selling platforms differently, paving the way to no…
BenchmarkingMovie RecommendationMultimodal RecommendationRecommendation Systems