mlsanityData to 7 Oct 2026

Influential Recommender System

arXiv 2211.10002 ↗First posted 18 Nov 2022cs.IR cs.AI cs.LGcited by 2

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Abstract (arXiv)

Traditional recommender systems are typically passive in that they try to adapt their recommendations to the user's historical interests. However, it is highly desirable for commercial applications, such as e-commerce, advertisement placement, and news portals, to be able to expand the users' interests so that they would accept items that they were not originally aware of or interested in to increase customer interactions. In this paper, we present Influential Recommender System (IRS), a new recommendation paradigm that aims to proactively lead a user to like a given objective item by progressively recommending to the user a sequence of carefully selected items (called an influence path). We propose the Influential Recommender Network (IRN), which is a Transformer-based sequential model to encode the items' sequential dependencies. Since different people react to external influences differently, we introduce the Personalized Impressionability Mask (PIM) to model how receptive a user is to external influence to generate the most effective influence path for the user. To evaluate IRN, we design several performance metrics to measure whether or not the influence path can smoothly expand the user interest to include the objective item while maintaining the user's satisfaction with the recommendation. Experimental results show that IRN significantly outperforms the baseline recommenders and demonstrates its capability of influencing users' interests.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 46 references that are in the map. First posted on arXiv 18 Nov 2022.

Builds on

22 of 46 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018Self-Attentive Sequential Recommendationcited by 1,169
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10 more
  1. 2019Sequential Recommender Systems: Challenges, Progress and Prospectscited by 115
  2. 2018Billion-scale Commodity Embedding for E-commerce Recommendation in Alibabacited by 105
  3. 2021Global Context Enhanced Graph Neural Networks for Session-based Recommendationcited by 81
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  5. 2017Translation-based Recommendationcited by 70
  6. 2018Multi-Head Attention with Disagreement Regularizationcited by 35
  7. 2019Effects of padding on LSTMs and CNNscited by 22
  8. 2019Towards Neural Mixture Recommender for Long Range Dependent User Sequencescited by 15
  9. 2019Sequential Evaluation and Generation Framework for Combinatorial Recommender Systemcited by 10
  10. 2021Easy and Efficient Transformer : Scalable Inference Solution For large NLP modelcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026ProRL: Effective Reinforcement Learning for Proactive Recommendation via Rectified Policy Gradient Estimationcited by 1
  2. 2026Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendationcited by 0