Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System

Temel Kavramlar

The author presents a solution for learning and optimizing implicit negative feedback in short-video recommendation systems, addressing challenges related to user preferences and multiple objectives. The proposed solution effectively extracts user preferences and optimizes model objectives, providing accurate recommendations.

Özet

The content discusses the challenges of learning from implicit negative feedback in short-video recommendation systems. It introduces a feedback-aware encoding module and a multi-objective prediction module to address these challenges. Extensive A/B tests confirm the effectiveness of the proposed solution.
The paper highlights the importance of implicit negative feedback in short-video recommendation systems due to skipping behavior. It addresses challenges such as extracting preference signals from feedback and dealing with multiple optimization goals.
By deploying a feedback-aware sequential encoder, the system can extract user preferences from mixed feedback types efficiently. Additionally, a multi-feedback prediction layer helps balance different optimization goals during prediction.
Online experiments on Kuaishou demonstrate that incorporating user negative feedback leads to improved performance metrics across various scenarios. The long-term analysis over six months shows steady improvements in content diversity based on users' negative feedback.
The study also compares performance across different engagement levels of users, showing consistent improvements with the proposed method. Future work includes combining sequential modeling with negative feedback learning for further enhancement.

İstatistikler

Specifically, users can choose to skip over the recommended video.
Users’ behav- iors will be fuzzier if most behaviors are just implicit skipping behaviors.
Watch time > 50% of other users.
Watch time < 3 seconds.
Given a user 𝑢 ∈ U, its history behavior sequences is denoted as S𝑢 = {𝑠𝑢1,𝑠𝑢2, · · · ,𝑠𝑢|S𝑢|}.
We develop a feature embedding layer that can utilize context information to filter useful prediction signals from sequential embeddings.
Context’s impact on user behav-iors depends on the specific user, item, and platform.
We design an embedding transform layer to learn the projection relationship between input embedding 𝐸in and specific context information 𝒆𝑢, 𝒆𝑖, 𝒆𝑝.
For example, young users (age attribute of user) may prefer to watch videos about electronics (category attributes of video) on the Web plat-form for a longer time (platform type).
We divide E(𝑢,𝑖,𝑝)trans into 𝐶 slots (the size of each slot is D/C and C is a hyper-parameter) and assign each slot different important weights (D/C dimensions inner one slot share the same weight).

Alıntılar

"Users’ behav-iors will be fuzzier if most behaviors are just implicit skipping behaviors."
"Given a user 𝑢 ∈ U, its history behavior sequences is denoted as S𝑢 = {𝑠1u , s2u , · · · , s|Su |}."
"We deploy a feature embedding layer that can utilize context information to filter useful prediction signals from sequential embeddings."

Önemli Bilgiler Şuradan Elde Edildi

Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System

by Yunzhu Pan,N... : arxiv.org 03-06-2024

https://arxiv.org/pdf/2308.13249.pdf

Daha Derin Sorular

How does incorporating implicit negative feedback impact overall user engagement?

Incorporating implicit negative feedback can have a significant impact on overall user engagement in recommendation systems. By considering users' skipping behaviors, such as the Glance Video Viewing (GVV) feedback in short-video platforms like Kuaishou, the system can better understand user preferences and tailor recommendations accordingly. This leads to more personalized and relevant content being recommended to users, increasing their satisfaction with the platform.
Implicit negative feedback also helps reduce explicit negative behaviors, such as dislikes or reports of similar recommendations. By addressing these issues proactively through modeling negative feedback, the system can improve user experience and retention. Additionally, by leveraging implicit signals like skipping behavior, the system can enhance recommendation accuracy and relevance for each individual user.

What are potential drawbacks or limitations of relying heavily on implicit negative feedback for recommendations?

While incorporating implicit negative feedback is beneficial for improving recommendation systems, there are some drawbacks and limitations to consider:

Limited Signal Interpretation: Implicit negative feedback like skipping behavior may not always clearly indicate user preferences. It could be due to various reasons other than disliking the content, such as distractions or interruptions during viewing.

Lack of Diversity: Relying solely on implicit negative feedback may lead to a lack of diversity in recommendations since it primarily focuses on avoiding disliked items rather than exploring new content that users might enjoy.

Overfitting: Depending too heavily on one type of signal (negative feedback) could potentially lead to overfitting the model to specific patterns present in that data but not necessarily reflective of broader user preferences.

User Frustration: If not balanced properly with positive signals or other types of interactions, an emphasis on implicit negative feedback could result in frustrating experiences for users who feel their interests are not accurately represented by the system.

How might advancements in understanding skipping behavior in music streaming platforms inform strategies for handling similar behavior in short-video recommendation systems?

Advancements in understanding skipping behavior from music streaming platforms can offer valuable insights into handling similar behaviors in short-video recommendation systems:

Behavioral Patterns Analysis: Insights gained from studying how users skip songs or videos can help identify common behavioral patterns across different types of media consumption platforms.

Feature Engineering: Techniques developed for analyzing skipping behavior data from music streaming services can be adapted and applied to short-video platforms for feature engineering and model development.

Personalization Strategies: Understanding why users skip certain content allows for more personalized strategies when recommending videos based on individual preferences and viewing habits.

Content Optimization: Leveraging insights from music streaming platforms can guide efforts towards optimizing video content delivery based on factors influencing skips or disengagement.

5Model Improvement: Lessons learned from addressing skips effectively within music contexts may inspire innovative approaches tailored specifically towards reducing skips while enhancing engagement within short-video environments.