Beyond Item Dissimilarities: Diversifying by Intent in Recommender Systems

• URL: http://arxiv.org/abs/2405.12327v3
• Date: Fri, 10 Jan 2025 18:30:45 GMT
• Title: Beyond Item Dissimilarities: Diversifying by Intent in Recommender Systems
• Authors: Yuyan Wang, Cheenar Banerjee, Samer Chucri, Fabio Soldo, Sriraj Badam, Ed H. Chi, Minmin Chen,
• Abstract summary: We develop a probabilistic intent-based whole-page diversification framework for the final stage of a recommender system. We experiment with the intent diversification framework on YouTube, the world's largest video recommendation platform.
• Score: 20.04619904064599
• License:
• Abstract: It has become increasingly clear that recommender systems that overly focus on short-term engagement prevents users from exploring diverse interests, ultimately hurting long-term user experience. To tackle this challenge, numerous diversification algorithms have been proposed. These algorithms typically rely on measures of item similarity, aiming to maximize the dissimilarity across items in the final set of recommendations. However, in this work, we demonstrate the benefits of going beyond item-level similarities by utilizing higher-level user understanding--specifically, user intents that persist across multiple interactions--in diversification. Our approach is motivated by the observation that user behaviors on online platforms are largely driven by their underlying intents. Therefore, recommendations should ensure that diverse user intents are accurately represented. While intent has primarily been studied in the context of search, it is less clear how to incorporate real-time dynamic intent predictions into recommender systems. To address this gap, we develop a probabilistic intent-based whole-page diversification framework for the final stage of a recommender system. Starting with a prior belief of user intents, the proposed framework sequentially selects items for each position based on these beliefs and subsequently updates posterior beliefs about the intents. This approach ensures that different user intents are represented on a page, towards optimizing long-term user experience. We experiment with the intent diversification framework on YouTube, the world's largest video recommendation platform, serving billions of users daily. Live experiments on a diverse set of intents show that the proposed framework increases Daily Active Users (DAU) and overall user enjoyment, validating its effectiveness in facilitating long-term planning.

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)
• Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations [15.143224593682012]
  We propose a novel recommendation strategy that combines relevance and diversity by a copula function. We use diversity as a surrogate of the amount of knowledge obtained by the user while interacting with the system. Our strategy outperforms several state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-07T13:48:24Z)
• Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement [5.734747179463411]
  We propose a Contrastive Learning sequential recommendation method based on Multi-Intention Disentanglement (MIDCL) In our work, intentions are recognized as dynamic and diverse, and user behaviors are often driven by current multi-intentions. We propose two types of contrastive learning paradigms for finding the most relevant user's interactive intention, and maximizing the mutual information of positive sample pairs.
  arXiv  Detail & Related papers  (2024-04-28T15:13:36Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions [0.5801044612920815]
  We propose an architecture that relies on common patterns as well as individual behaviors to tailor its recommendations for each person. Our empirical results on Nielsen Consumer Panel dataset indicate that the proposed approach achieves up to 27.9% performance improvement.
  arXiv  Detail & Related papers  (2022-09-16T12:07:43Z)
• Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model [18.109035867113217]
  Multi-Interest Preference (MIP) model is an approach that produces multi-interest for users by using the user's sequential engagement more effectively. Extensive experiments have been done on various industrial-scale datasets to demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-07-14T04:29:54Z)
• Intent Contrastive Learning for Sequential Recommendation [86.54439927038968]
  We introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering. We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding intent. Experiments conducted on four real-world datasets demonstrate the superiority of the proposed learning paradigm.
  arXiv  Detail & Related papers  (2022-02-05T09:24:13Z)
• Sparse-Interest Network for Sequential Recommendation [78.83064567614656]
  We propose a novel textbfSparse textbfInterest textbfNEtwork (SINE) for sequential recommendation. Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool. SINE can achieve substantial improvement over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-18T11:03:48Z)
• PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest [54.56236567783225]
  PinnerSage is an end-to-end recommender system that represents each user via multi-modal embeddings. We conduct several offline and online A/B experiments to show that our method significantly outperforms single embedding methods.
  arXiv  Detail & Related papers  (2020-07-07T17:13:20Z)
• Controllable Multi-Interest Framework for Recommendation [64.30030600415654]
  We formalize the recommender system as a sequential recommendation problem. We propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our framework has been successfully deployed on the offline Alibaba distributed cloud platform.
  arXiv  Detail & Related papers  (2020-05-19T10:18:43Z)

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.