FEASE: Shallow AutoEncoding Recommender with Cold Start Handling via Side Features

• URL: http://arxiv.org/abs/2504.02288v2
• Date: Fri, 04 Apr 2025 15:01:46 GMT
• Title: FEASE: Shallow AutoEncoding Recommender with Cold Start Handling via Side Features
• Authors: Edward DongBo Cui, Lu Zhang, William Ping-hsun Lee,
• Abstract summary: User and item cold starts present significant challenges in industrial applications of recommendation systems.<n>We introduce an augmented EASE model, i.e. FEASE, that seamlessly integrates both user and item side information.<n>Our method strikes a balance by effectively recommending cold start items and handling cold start users without incurring extra bias.
• Score: 2.8680286413498903
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: User and item cold starts present significant challenges in industrial applications of recommendation systems. Supplementing user-item interaction data with metadata is a common solution-but often at the cost of introducing additional biases. In this work, we introduce an augmented EASE model, i.e. FEASE, that seamlessly integrates both user and item side information to address these cold start issues. Our straightforward, autoencoder-based method produces a closed-form solution that leverages rich content signals for cold items while refining user representations in data-sparse environments. Importantly, our method strikes a balance by effectively recommending cold start items and handling cold start users without incurring extra bias, and it maintains strong performance in warm settings. Experimental results demonstrate improved recommendation accuracy and robustness compared to previous collaborative filtering approaches. Moreover, our model serves as a strong baseline for future comparative studies.

Related papers

• Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User [117.82681846559909]
  Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations. We propose a generative reward model based simulated user, named GRSU, for automatic interaction with CRSs.
  arXiv  Detail & Related papers  (2025-04-29T06:37:30Z)
• Prompt Tuning for Item Cold-start Recommendation [21.073232866618554]
  The item cold-start problem is crucial for online recommender systems, as the success of the cold-start phase determines whether items can transition into popular ones.<n> Prompt learning, a powerful technique used in natural language processing (NLP) to address zero- or few-shot problems, has been adapted for recommender systems to tackle similar challenges.<n>We propose to leverage high-value positive feedback, termed pinnacle feedback as prompt information, to simultaneously resolve the above two problems.
  arXiv  Detail & Related papers  (2024-12-24T01:38:19Z)
• Firzen: Firing Strict Cold-Start Items with Frozen Heterogeneous and Homogeneous Graphs for Recommendation [34.414081170244955]
  We propose a unified framework incorporating multi-modal content of items and knowledge graphs (KGs) to solve both strict cold-start and warm-start recommendation. Our model yields significant improvements for strict cold-start recommendation and outperforms or matches the state-of-the-art performance in the warm-start scenario.
  arXiv  Detail & Related papers  (2024-10-10T06:48:27Z)
• Large Language Model Simulator for Cold-Start Recommendation [45.34030399042562]
  Cold items rely solely on content features, limiting their recommendation performance and impacting user experience and revenue.<n>Current models generate synthetic behavioral embeddings from content features but fail to address the core issue: the absence of historical behavior data.<n>We introduce the LLM Simulator framework, which leverages large language models to simulate user interactions for cold items.
  arXiv  Detail & Related papers  (2024-02-14T13:45:06Z)
• Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems [10.133475523630139]
  Cold-start recommendation is one of the major challenges faced by recommender systems (RS) In this paper, we propose Cold & Warm Net based on expert models who are responsible for modeling cold-start and warm-up users respectively. The proposed model has also been deployed on an industrial short video platform and achieves a significant increase in app dwell time and user retention rate.
  arXiv  Detail & Related papers  (2023-09-27T13:31:43Z)
• 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)
• Sparsity Regularization For Cold-Start Recommendation [7.848143873095096]
  We introduce a novel representation for user-vectors by combining user demographics and user preferences. We develop a novel sparse adversarial model, SRLGAN, for Cold-Start Recommendation leveraging the sparse user-purchase behavior. We evaluate the SRLGAN on two popular datasets and demonstrate state-of-the-art results.
  arXiv  Detail & Related papers  (2022-01-26T02:28:08Z)
• 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)
• Privileged Graph Distillation for Cold Start Recommendation [57.918041397089254]
  The cold start problem in recommender systems requires recommending to new users (items) based on attributes without any historical interaction records. We propose a privileged graph distillation model(PGD) Our proposed model is generally applicable to different cold start scenarios with new user, new item, or new user-new item.
  arXiv  Detail & Related papers  (2021-05-31T14:05:27Z)
• Cold-start Sequential Recommendation via Meta Learner [10.491428090228768]
  We propose a Meta-learning-based Cold-Start Sequential Recommendation Framework, namely Mecos, to mitigate the item cold-start problem in sequential recommendation. Mecos effectively extracts user preference from limited interactions and learns to match the target cold-start item with the potential user.
  arXiv  Detail & Related papers  (2020-12-10T05:23:13Z)
• Modurec: Recommender Systems with Feature and Time Modulation [50.51144496609274]
  We propose Modurec: an autoencoder-based method that combines all available information using the feature-wise modulation mechanism. We show on Movielens datasets that these modifications produce state-of-the-art results in most evaluated settings.
  arXiv  Detail & Related papers  (2020-10-13T09:18:33Z)
• Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users [111.28351584726092]
  We consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play.
  arXiv  Detail & Related papers  (2020-05-23T08:56:37Z)

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.