Content-Based Personalized Recommender System Using Entity Embeddings

• URL: http://arxiv.org/abs/2010.12798v1
• Date: Sat, 24 Oct 2020 06:25:13 GMT
• Title: Content-Based Personalized Recommender System Using Entity Embeddings
• Authors: Xavier Thomas
• Abstract summary: This paper aims to highlight the advantages of the content-based approach through learned embeddings. It provides better and personalised movie recommendations based on user preferences to various movie features such as genre and keyword tags.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems are a class of machine learning algorithms that provide relevant recommendations to a user based on the user's interaction with similar items or based on the content of the item. In settings where the content of the item is to be preserved, a content-based approach would be beneficial. This paper aims to highlight the advantages of the content-based approach through learned embeddings and leveraging these advantages to provide better and personalised movie recommendations based on user preferences to various movie features such as genre and keyword tags.

Related papers

• Preference Elicitation with Soft Attributes in Interactive Recommendation [39.74528988497788]
  We develop novel preference elicitation methods that can accommodate soft attributes and bring together item and attribute-based preference elicitation. Our techniques query users using both items and soft attributes to update the recommender system's belief about their preferences to improve recommendation quality.
  arXiv  Detail & Related papers  (2023-10-22T17:23:20Z)
• VideolandGPT: A User Study on a Conversational Recommender System [0.14495144578817493]
  We introduce VideolandGPT, a recommender system for a Video-on-Demand (VOD) platform, Videoland, which uses ChatGPT to select from a predetermined set of contents. We evaluate ranking metrics, user experience, and fairness of recommendations, comparing a personalised and a non-personalised version of the system.
  arXiv  Detail & Related papers  (2023-09-07T11:24:47Z)
• PLIERS: a Popularity-Based Recommender System for Content Dissemination in Online Social Networks [5.505634045241288]
  We propose a novel tag-based recommender system called PLIERS. It relies on the assumption that users are mainly interested in items and tags with similar popularity to those they already own. PLIERS is aimed at reaching a good tradeoff between algorithmic and the level of personalization of recommended items.
  arXiv  Detail & Related papers  (2023-07-06T09:04:58Z)
• Editable User Profiles for Controllable Text Recommendation [66.00743968792275]
  We propose LACE, a novel concept value bottleneck model for controllable text recommendations. LACE represents each user with a succinct set of human-readable concepts. It learns personalized representations of the concepts based on user documents.
  arXiv  Detail & Related papers  (2023-04-09T14:52:18Z)
• Dual Personalization on Federated Recommendation [50.4115315992418]
  Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models. We also propose a new dual personalization mechanism to effectively learn fine-grained personalization on both users and items.
  arXiv  Detail & Related papers  (2023-01-16T05:26:07Z)
• Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attention [52.84233165201391]
  We propose a method to detect individualized highlights for users on given target videos based on their preferred highlight clips marked on previous videos they have watched. Our method explicitly leverages the contents of both the preferred clips and the target videos using pre-trained features for the objects and the human activities.
  arXiv  Detail & Related papers  (2022-07-18T02:32:48Z)
• Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors [34.56323846959459]
  Interactive recommender systems allow users to express intent, preferences, constraints, and contexts in a richer fashion. One challenge is inferring a user's semantic intent from the open-ended terms or attributes often used to describe a desired item. We develop a framework to learn a representation that captures the semantics of such attributes and connects them to user preferences and behaviors in recommender systems.
  arXiv  Detail & Related papers  (2022-02-06T18:45:15Z)
• 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)
• Designing Explanations for Group Recommender Systems [0.0]
  Explanations are used in recommender systems for various reasons. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended.
  arXiv  Detail & Related papers  (2021-02-24T17:05:39Z)
• Deep Learning feature selection to unhide demographic recommender systems factors [63.732639864601914]
  The matrix factorization model generates factors which do not incorporate semantic knowledge. DeepUnHide is able to extract demographic information from the users and items factors in collaborative filtering recommender systems.
  arXiv  Detail & Related papers  (2020-06-17T17:36:48Z)

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.