Movie Recommender System using critic consensus

• URL: http://arxiv.org/abs/2112.11854v1
• Date: Wed, 22 Dec 2021 13:04:41 GMT
• Title: Movie Recommender System using critic consensus
• Authors: A Nayan Varma, Kedareshwara Petluri
• Abstract summary: We propose a hybrid recommendation system based on the integration of collaborative and content-based content. We would like to present a novel model that recommends movies based on the combination of user preferences and critical consensus scores.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recommendation systems are perhaps one of the most important agents for industry growth through the modern Internet world. Previous approaches on recommendation systems include collaborative filtering and content based filtering recommendation systems. These 2 methods are disjointed in nature and require the continuous storage of user preferences for a better recommendation. To provide better integration of the two processes, we propose a hybrid recommendation system based on the integration of collaborative and content-based content, taking into account the top critic consensus and movie rating score. We would like to present a novel model that recommends movies based on the combination of user preferences and critical consensus scores.

Related papers

• Post-Userist Recommender Systems : A Manifesto [1.7157586976839874]
  We define userist recommendation as an approach to recommender systems framed solely in terms of the relation between the user and system. Post-userist recommendation posits a larger field of relations in which stakeholders are embedded and distinguishes the recommendation function from generative media.
  arXiv  Detail & Related papers  (2024-10-09T03:16:37Z)
• End-to-End Learnable Item Tokenization for Generative Recommendation [51.82768744368208]
  We propose ETEGRec, a novel End-To-End Generative Recommender by seamlessly integrating item tokenization and generative recommendation. Our framework is developed based on the dual encoder-decoder architecture, which consists of an item tokenizer and a generative recommender.
  arXiv  Detail & Related papers  (2024-09-09T12:11:53Z)
• A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation [77.42486522565295]
  We propose a novel recommendation approach called LSVCR to jointly conduct personalized video and comment recommendation. Our approach consists of two key components, namely sequential recommendation (SR) model and supplemental large language model (LLM) recommender. In particular, we achieve a significant overall gain of 4.13% in comment watch time.
  arXiv  Detail & Related papers  (2024-03-20T13:14:29Z)
• Breaking Feedback Loops in Recommender Systems with Causal Inference [99.22185950608838]
  Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior. We propose the Causal Adjustment for Feedback Loops (CAFL), an algorithm that provably breaks feedback loops using causal inference. We show that CAFL improves recommendation quality when compared to prior correction methods.
  arXiv  Detail & Related papers  (2022-07-04T17:58:39Z)
• A Review on Pushing the Limits of Baseline Recommendation Systems with the integration of Opinion Mining & Information Retrieval Techniques [0.0]
  Recommendation Systems allow users to identify trending items among a community while being timely and relevant to the user's expectations. Deep Learning methods have been brought forward to achieve better quality recommendations. Researchers have tried to expand on the capabilities of standard recommendation systems to provide the most effective recommendations.
  arXiv  Detail & Related papers  (2022-05-03T22:13:33Z)
• FEBR: Expert-Based Recommendation Framework for beneficial and personalized content [77.86290991564829]
  We propose FEBR (Expert-Based Recommendation Framework), an apprenticeship learning framework to assess the quality of the recommended content. The framework exploits the demonstrated trajectories of an expert (assumed to be reliable) in a recommendation evaluation environment, to recover an unknown utility function. We evaluate the performance of our solution through a user interest simulation environment (using RecSim)
  arXiv  Detail & Related papers  (2021-07-17T18:21:31Z)
• A Hybrid Recommender System for Recommending Smartphones to Prospective Customers [0.7310043452300736]
  Hybrid recommender systems combine multiple recommendation strategies in different ways to benefit from their complementary advantages. Some hybrid recommender systems have combined collaborative filtering and content-based approaches to build systems that are more robust. We propose a hybrid recommender system, which combines Alternative Least Squares (ALS) based collaborative filtering with deep learning to enhance recommendation performance.
  arXiv  Detail & Related papers  (2021-05-26T23:10:51Z)
• INSPIRED: Toward Sociable Recommendation Dialog Systems [51.1063713492648]
  In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. We present a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations. Our analysis shows that sociable recommendation strategies, such as sharing personal opinions or communicating with encouragement, more frequently lead to successful recommendations.
  arXiv  Detail & Related papers  (2020-09-29T21:03:44Z)
• Hotel Recommendation System Based on User Profiles and Collaborative Filtering [0.0]
  This paper presents a new hybrid hotel recommendation system that has been developed by combining content-based and collaborative filtering approaches. The resulting system is known as a hybrid recommender system.
  arXiv  Detail & Related papers  (2020-09-21T09:57:54Z)
• Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [48.2733163413522]
  It becomes critical to embrace a trustworthy recommender system. This survey provides a systemic summary of three categories of trust-aware recommender systems.
  arXiv  Detail & Related papers  (2020-04-08T02:11:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.