Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models

• URL: http://arxiv.org/abs/2502.13539v1
• Date: Wed, 19 Feb 2025 08:47:42 GMT
• Title: Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
• Authors: Yunjia Xi, Muyan Weng, Wen Chen, Chao Yi, Dian Chen, Gaoyang Guo, Mao Zhang, Jian Wu, Yuning Jiang, Qingwen Liu, Yong Yu, Weinan Zhang,
• Abstract summary: Large language models (LLMs) have shown potential in serendipity prediction due to their extensive world knowledge and reasoning capabilities. We propose SERAL, a framework comprising three stages: Cognition Profile Generation, SerenGPT Alignment, and Nearline Adaptation. Online experiments demonstrate that SERAL improves exposure ratio (PVR), clicks, and transactions of serendipitous items by 5.7%, 29.56%, and 27.6%, enhancing user experience without much impact on overall revenue.
• Score: 42.13005951072714
• License:
• Abstract: Recommender systems (RSs) often suffer from the feedback loop phenomenon, e.g., RSs are trained on data biased by their recommendations. This leads to the filter bubble effect that reinforces homogeneous content and reduces user satisfaction. To this end, serendipity recommendations, which offer unexpected yet relevant items, are proposed. Recently, large language models (LLMs) have shown potential in serendipity prediction due to their extensive world knowledge and reasoning capabilities. However, they still face challenges in aligning serendipity judgments with human assessments, handling long user behavior sequences, and meeting the latency requirements of industrial RSs. To address these issues, we propose SERAL (Serendipity Recommendations with Aligned Large Language Models), a framework comprising three stages: (1) Cognition Profile Generation to compress user behavior into multi-level profiles; (2) SerenGPT Alignment to align serendipity judgments with human preferences using enriched training data; and (3) Nearline Adaptation to integrate SerenGPT into industrial RSs pipelines efficiently. Online experiments demonstrate that SERAL improves exposure ratio (PVR), clicks, and transactions of serendipitous items by 5.7%, 29.56%, and 27.6%, enhancing user experience without much impact on overall revenue. Now, it has been fully deployed in the "Guess What You Like" of the Taobao App homepage.

Related papers

• Interactive Visualization Recommendation with Hier-SUCB [52.11209329270573]
  We propose an interactive personalized visualization recommendation (PVisRec) system that learns on user feedback from previous interactions. For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual semi-bandit in the PVisRec setting.
  arXiv  Detail & Related papers  (2025-02-05T17:14:45Z)
• Prompt Tuning as User Inherent Profile Inference Machine [53.78398656789463]
  We propose UserIP-Tuning, which uses prompt-tuning to infer user profiles. A profile quantization codebook bridges the modality gap by profile embeddings into collaborative IDs. Experiments on four public datasets show that UserIP-Tuning outperforms state-of-the-art recommendation algorithms.
  arXiv  Detail & Related papers  (2024-08-13T02:25:46Z)
• Treatment Effect Estimation for User Interest Exploration on Recommender Systems [10.05609996672672]
  We propose an Uplift model-based Recommender framework, which regards top-N recommendation as a treatment optimization problem. UpliftRec estimates the treatment effects, i.e., the click-through rate (CTR) under different category exposure ratios, by using observational user feedback. UpliftRec calculates group-level treatment effects to discover users' hidden interests with high CTR rewards.
  arXiv  Detail & Related papers  (2024-05-14T13:22:33Z)
• FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems [15.463595798992621]
  FedRec+ is an ensemble framework for federated recommendation systems. It enhances privacy and reduces communication costs for edge users. Experimental results demonstrate the state-of-the-art performance of FedRec+.
  arXiv  Detail & Related papers  (2023-10-31T05:36:53Z)
• BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by Eliminating Ideological Segregation in Knowledge-based Recommendations [5.795636579831129]
  The main objective is to strike a belief balance for users while minimizing the detrimental influence caused by filter bubbles. The BHEISR model amalgamates principles from nudge theory while upholding democratic and transparent principles.
  arXiv  Detail & Related papers  (2023-07-06T06:12:37Z)
• When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation [56.98380787425388]
  We propose a search-Enhanced framework for the Sequential Recommendation (SESRec) SESRec disentangling similar and dissimilar representations within S&R behaviors. Experiments on both industrial and public datasets demonstrate that SESRec consistently outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2023-05-18T09:04:50Z)
• Generative Slate Recommendation with Reinforcement Learning [49.75985313698214]
  reinforcement learning algorithms can be used to optimize user engagement in recommender systems. However, RL approaches are intractable in the slate recommendation scenario. In that setting, an action corresponds to a slate that may contain any combination of items. In this work we propose to encode slates in a continuous, low-dimensional latent space learned by a variational auto-encoder. We are able to (i) relax assumptions required by previous work, and (ii) improve the quality of the action selection by modeling full slates.
  arXiv  Detail & Related papers  (2023-01-20T15:28:09Z)
• Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems [9.281057513518498]
  serendipity-oriented recommender system generates users' self-serendipity preferences to enhance recommendation performance. Model extracts users' interest and satisfaction preferences, generates virtual but convincible neighbors' preferences from themselves, and achieves their self-serendipity preference.
  arXiv  Detail & Related papers  (2022-04-27T01:29:47Z)
• PURS: Personalized Unexpected Recommender System for Improving User Satisfaction [76.98616102965023]
  We describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches.
  arXiv  Detail & Related papers  (2021-06-05T01:33:21Z)

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.