Dissertation: On the Theoretical Foundation of Model Comparison and Evaluation for Recommender System

• URL: http://arxiv.org/abs/2411.01843v1
• Date: Mon, 04 Nov 2024 06:31:52 GMT
• Title: Dissertation: On the Theoretical Foundation of Model Comparison and Evaluation for Recommender System
• Authors: Dong Li,
• Abstract summary: Recommender systems utilize users' historical data to infer customer interests and provide personalized recommendations. Collaborative filtering is one family of recommendation algorithms that uses ratings from multiple users to predict missing ratings. Recommender systems can be more complex and incorporate auxiliary data such as content-based attributes, user interactions, and contextual information.
• Score: 4.76281731053599
• License:
• Abstract: Recommender systems have become increasingly important with the rise of the web as a medium for electronic and business transactions. One of the key drivers of this technology is the ease with which users can provide feedback about their likes and dislikes through simple clicks of a mouse. This feedback is commonly collected in the form of ratings, but can also be inferred from a user's browsing and purchasing history. Recommender systems utilize users' historical data to infer customer interests and provide personalized recommendations. The basic principle of recommendations is that significant dependencies exist between user- and item-centric activity, which can be learned in a data-driven manner to make accurate predictions. Collaborative filtering is one family of recommendation algorithms that uses ratings from multiple users to predict missing ratings or uses binary click information to predict potential clicks. However, recommender systems can be more complex and incorporate auxiliary data such as content-based attributes, user interactions, and contextual information.

Related papers

• Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives [11.835903510784735]
  Review-based recommender systems have emerged as a significant sub-field in this domain. We present a categorization of these systems and summarize the state-of-the-art methods, analyzing their unique features, effectiveness, and limitations. We propose potential directions for future research, including the integration of multimodal data, multi-criteria rating information, and ethical considerations.
  arXiv  Detail & Related papers  (2024-05-09T05:45:18Z)
• The Fault in Our Recommendations: On the Perils of Optimizing the Measurable [2.6217304977339473]
  We show that optimizing for engagement can lead to significant utility losses. We propose a utility-aware policy that initially recommends a mix of popular and niche content.
  arXiv  Detail & Related papers  (2024-05-07T02:12:17Z)
• User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations [96.45414741693119]
  We present a user-controllable recommender system that seamlessly integrates explainability and controllability. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system.
  arXiv  Detail & Related papers  (2023-08-02T01:13:36Z)
• 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)
• FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings [108.77460689459247]
  We propose a Federated Graph Recommender System (FedGRec) to mitigate privacy concerns. In our system, users and the server explicitly store latent embeddings for users and items, where the latent embeddings summarize different orders of indirect user-item interactions. We perform extensive empirical evaluations to verify the efficacy of using latent embeddings as a proxy of missing interaction graph.
  arXiv  Detail & Related papers  (2022-10-25T01:08:20Z)
• 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)
• Causal Disentanglement with Network Information for Debiased Recommendations [34.698181166037564]
  Recent research proposes to debias by modeling a recommender system from a causal perspective. The critical challenge in this setting is accounting for the hidden confounders. We propose to leverage network information (i.e., user-social and user-item networks) to better approximate hidden confounders.
  arXiv  Detail & Related papers  (2022-04-14T20:55:11Z)
• Improving Rating and Relevance with Point-of-Interest Recommender System [0.0]
  We develop a deep neural network architecture to model query-item relevance in the presence of both collaborative and content information. The application of these learned representations to a large-scale dataset resulted in significant improvements.
  arXiv  Detail & Related papers  (2022-02-17T16:43:17Z)
• Learning to Learn a Cold-start Sequential Recommender [70.5692886883067]
  The cold-start recommendation is an urgent problem in contemporary online applications. We propose a meta-learning based cold-start sequential recommendation framework called metaCSR. metaCSR holds the ability to learn the common patterns from regular users' behaviors.
  arXiv  Detail & Related papers  (2021-10-18T08:11:24Z)
• Click-Through Rate Prediction Using Graph Neural Networks and Online Learning [0.0]
  A small percent improvement on the CTR prediction accuracy has been mentioned to add millions of dollars of revenue to the advertisement industry. This project is interested in building a CTR predictor using Graph Neural Networks and an online learning algorithm.
  arXiv  Detail & Related papers  (2021-05-09T01:35:49Z)
• A Bayesian Approach to Conversational Recommendation Systems [60.12942570608859]
  We present a conversational recommendation system based on a Bayesian approach. A case study based on the application of this approach to emphstagend.com, an online platform for booking entertainers, is discussed.
  arXiv  Detail & Related papers  (2020-02-12T15:59:31Z)

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.