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Proactive Recommendation with Iterative Preference Guidance: A Framework for Guiding User Interests


Core Concepts
The author introduces an Iterative Preference Guidance (IPG) framework to address the limitations of traditional recommender systems and actively guide users towards new interests. IPG ranks items based on their IPG scores, considering both interaction probability and guiding value.
Abstract
The content discusses the challenges faced by traditional recommender systems in catering to user interests and introduces the Iterative Preference Guidance (IPG) framework as a solution. IPG aims to steer users towards developing new interests by strategically modulating recommendation sequences. The framework considers user feedback, guiding objectives, and flexibility for integration into existing industrial recommender systems. By explicitly estimating IPG scores, IPG effectively guides user interests towards target interests while maintaining reasonable accuracy in recommendations. Experimental validation confirms the effectiveness of IPG in preference guidance. Traditional recommender systems passively cater to user interests, leading to issues like filter bubbles and opinion polarization. Proactive recommendation through the IPG framework actively steers users towards developing new interests by ranking items based on their IPG scores. This approach considers both interaction probability and guiding value, resulting in effective guidance towards target interests with a reasonable trade-off in accuracy.
Stats
Extensive experiments validate that IPG can effectively guide user interests toward target interests with a reasonable trade-off in recommender accuracy. The dataset used has 6034 users and 3533 items, with an average of 26.9 positive interactions per user. Hit Ratio (HR@K) metric is used to quantify the proportion of items interacted positively over K rounds of recommendation. Increase of Interest (IoI@K) metric assesses the average increase in user preference towards the target item following K rounds of recommendations. SASRec-IPG outperforms all baseline models in terms of IoI@K metric across different values of ๐›พ.
Quotes
"We emphasize the necessity of proactive recommendation and utilize real-time user feedback for iterative preference guidance." "IPG is model-agnostic and introduces no additional training objectives or parameters." "Experimental results showed that IPG can achieve a significant improvement in guiding usersโ€™ interests while maintaining a reasonable level of recommendation accuracy."

Key Insights Distilled From

by Shuxian Bi,W... at arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07571.pdf
Proactive Recommendation with Iterative Preference Guidance

Deeper Inquiries

How can proactive recommendation strategies like IPG impact long-term user engagement beyond immediate preferences

Proactive recommendation strategies like IPG can have a significant impact on long-term user engagement beyond immediate preferences by fostering user exploration and diversification. By guiding users towards new interests or topics, these strategies can help prevent filter bubbles and echo chambers that limit user exposure to diverse content. This proactive approach encourages users to discover novel items they may not have encountered otherwise, leading to increased engagement with the platform over time. Additionally, by steering users towards a target item or topic strategically, IPG can facilitate serendipitous discoveries and enhance overall user satisfaction with the recommendations provided.

What potential drawbacks or criticisms could be raised against the concept of proactive recommendation frameworks like IPG

While proactive recommendation frameworks like IPG offer several benefits in terms of enhancing user engagement and diversifying preferences, there are potential drawbacks and criticisms that could be raised: Oversteering: One criticism could be that overly aggressive guidance towards a target item might lead to a loss of personalization or individual autonomy in choice. Users may feel restricted or manipulated if their preferences are consistently directed towards specific items without considering their unique tastes. Limited Serendipity: Proactive recommendation strategies focus on guiding users based on predefined objectives, which could potentially limit opportunities for serendipitous discoveries or unexpected recommendations that align with evolving interests. Privacy Concerns: The iterative nature of preference guidance in frameworks like IPG requires continuous monitoring of user interactions and feedback data. This raises privacy concerns regarding the collection and utilization of sensitive information for personalized recommendations.

How might advancements in large language models influence the evolution and effectiveness of frameworks like IPG

Advancements in large language models (LLMs) can significantly influence the evolution and effectiveness of frameworks like IPG by enabling more sophisticated understanding of user preferences and context: Enhanced User Understanding: LLMs can leverage vast amounts of text data to develop deeper insights into user behavior, preferences, and intent. This enhanced understanding allows proactive recommendation systems to tailor guidance more effectively based on nuanced signals from users' interactions. Improved Natural Language Processing: With advancements in NLP capabilities offered by LLMs, proactive recommendation frameworks can better interpret textual cues from reviews, comments, or other forms of feedback to refine the guidance strategy dynamically. Contextual Recommendations: LLMs excel at capturing contextual information from unstructured text data sources such as social media posts or product reviews. By incorporating this contextual understanding into proactive recommendation models like IPG, platforms can deliver more relevant suggestions aligned with users' current needs and interests. These advancements highlight how leveraging LLM technologies can enhance the sophistication and adaptability of proactive recommendation frameworks like IPG in catering to evolving user preferences effectively while ensuring personalized experiences for each individual.user
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