IF-VR: Video Rec Benchmark with Implicit Feedback

Updated 11 August 2025

The paper introduces the IF-VR benchmark that uses implicit feedback signals to evaluate video recommendation systems in a reproducible and fair manner.
It employs multi-modal datasets and advanced context-aware methods, including sequential and graph-based modeling, to robustly handle noisy user interactions.
Industrial deployment of IF-VR techniques shows improved real-time adaptation, cold-start handling, and fairness in content recommendation.

Video Recommendation Benchmark with Implicit Feedback (IF-VR) defines a research and evaluation paradigm in which personalized video recommender systems are trained, tuned, and measured exclusively or primarily through non-explicit user signals—such as viewing durations, skips, swipe behaviors, and other passively observed interactions—rather than explicit feedback like ratings or binary likes. Driven by industrial-scale applications (e.g., streaming or social video platforms) and the need for more rigorously reproducible experimentation, IF-VR benchmarks encapsulate methodologies, datasets, algorithms, and metrics purpose-built to advance the empirical paper of implicit feedback modeling in video recommendation.

1. Definition and Motivation

IF-VR encompasses benchmark datasets, evaluation protocols, and system designs that leverage only implicit feedback—e.g., view percentage matrices, clicks, skip rates, play completions—to infer user preferences over video content in large catalogs. The rationale is that real-world video platforms (e.g., YouTube, Kuaishou, Facebook Watch, Tencent Video) rarely obtain rich explicit feedback, necessitating algorithmic developments specifically tailored to the unique statistical properties and noise characteristics of implicit feedback (Hidasi et al., 2012, Liu et al., 2018, Chen et al., 7 Aug 2025). IF-VR benchmarks address:

The absence of direct ratings or labeling, requiring learning from weak, noisy, and highly imbalanced signals.
The need for context-aware, cold-start resilient methods due to fast-evolving catalogs and diverse user patterns.
A requirement for reproducible, fair, and extensible experimental setups to compare algorithms meaningfully across tasks, models, and datasets (Nazary et al., 6 Aug 2025, Fang et al., 14 Aug 2024).

2. Dataset Construction and Benchmark Design

Constructing an effective IF-VR benchmark involves selecting or creating datasets that reflect the operational realities of video services:

Scale and Sparsity: Datasets such as IF-VR contain millions of records, with sample sizes in published benchmarks ranging from 15,000 users × 25,000 videos × 933,000 interactions for multi-modal video (with additional ~72,000 annotated skip explanations, ~50,000 explicit dislikes) (Chen et al., 7 Aug 2025).
Multi-Modality: IF-VR datasets now frequently include multi-modal features—video frames, audio tracks, textual metadata, and in some cases, LLM-augmented synopses—to enable and assess content-aware and hybrid models (Nazary et al., 6 Aug 2025, Liu, 2022).
Interaction Encoding: Instead of ratings matrices, IF-VR employs behavioral matrices where entries may represent viewing percentage (Maghsoudi et al., 2023), skip/engage/focus event sequences (Pan et al., 2023), clicks, play durations, or even session logs with temporal, device, or context metadata (Hidasi et al., 2012, Gong et al., 2022).
Annotation and Reasoning Labels: IF-VR benchmarks may enrich data with reasoning explanations for skip behaviors using LLM-annotated and human-screened rationales (e.g., user disliked genre, accident, etc.) (Chen et al., 7 Aug 2025).
Task Coverage: Tasks include sequential next-click prediction, top-N recommendation, explicit dislike simulation, and click-through/play prediction, reflecting practical scenarios in both feed ranking and session-based video services.

3. Core Methodological Advances

The emergence of IF-VR benchmarks catalyzed advances in context-aware, robust, and scalable implicit feedback modeling:

Context and Sequential Modeling: Tensor factorization frameworks (e.g., iTALS) enable explicit incorporation of temporal bands and sequential context, supporting diurnal or session-based adaptations (Hidasi et al., 2012).
Cost-Sensitive and Decompositional Methods: Robust loss functions with asymmetric misclassification penalties, low-rank + sparse decompositions, and cost-sensitive learning (e.g., CSRR) are leveraged to address the severe imbalance between positive/engaged and negative/skipped events (Yang et al., 2017).
Graph-based and Group-aware Frameworks: Graph-Refined Convolutional Networks (e.g., GRCN) adaptively refit user–item interaction graphs to prune spurious (often noisy) edges from implicit logs, using content-driven affinity scores for denoising (Yinwei et al., 2021). Group-aware RL (G-UBS) clusters user behaviors, using group-level knowledge to mitigate noise in individual implicit signals via reinforcement learning (Chen et al., 7 Aug 2025).
Reinforcement Learning and User Simulation: Recent IF-VR benchmarks enable reinforcement learning from implicit signals, often leveraging LLM/MLLM-based user simulation (e.g., VRAgent-R1) where user feedback and choices are simulated with chain-of-thought reasoning + RL reward mechanisms, closing the loop between model output and user outcomes (Chen et al., 3 Jul 2025).
Multi-objective and Multi-modal Learning: Modeling both positive (engaged/focus/view) and negative (skip) behaviors as multi-objective tasks (with feedback-aware encoders) allows systems to balance trade-offs between increasing dwell time and reducing early abandonment (Pan et al., 2023, Liu, 2022, Nazary et al., 6 Aug 2025).
Hyperparameter Optimization and Fair Comparison: Systematic studies using multi-type hyperparameter search (e.g., BOHB, TPE, Hyperband) ensure that results in IF-VR are fairly comparable and robust to parameter selection (Fang et al., 14 Aug 2024).

4. Evaluation Metrics and Protocols

IF-VR benchmarks emphasize both classical and beyond-accuracy metrics suited to implicit interaction data:

Ranking-based Metrics: Recall@K, NDCG@K, Precision@K report success in retrieving relevant or engaging videos within short recommendation lists, matching user-facing presentation (Hidasi et al., 2012, Askari et al., 2020, Nazary et al., 6 Aug 2025).
User Engagement Metrics: Play rate above a threshold (e.g., % of videos watched > 30% duration), click-through rate (CTR), finish rate, effective view, like/follow rates (Chen et al., 7 Aug 2025, Gong et al., 2022, Maghsoudi et al., 2023).
Novelty, Coverage, Diversity, and Fairness: Mean and median of negative log-popularity (novelty), catalog coverage (fraction of items recommended), intra-list diversity (embedding distance), and distributional fairness (category, genre parity) (Nazary et al., 6 Aug 2025, Liu et al., 2023).
Reasoning Accuracy: The capacity to predict both the behavioral outcome (e.g., will skip) and the underlying reason (e.g., user dislikes, accident), measured via F1/Accuracy on labeled skip rationale records (Chen et al., 7 Aug 2025).
Resource Allocation and Repeatability: Official codebases enforce declarative YAML-based experiment specifications and publish all precomputed features, enabling standardized, reproducible evaluation (Nazary et al., 6 Aug 2025).

5. Algorithmic Impact and Industrial Adoption

Methodologies proven on IF-VR benchmarks translate directly to large-scale deployment:

Real-time Adaptation: On-device, low-latency ranking engines adapt to new implicit feedback instantaneously, as in Kuaishou’s billion-user system, using cross-feature engineering and beam-search for list-wise reranking (Gong et al., 2022).
Noise Tolerance: Group-aware and denoising models (e.g., G-UBS, GRCN) substantially outperform generic LLMs/MLLMs in high-noise regimes common in open video feeds (achieving ~4% higher play rate and ~15% reasoning accuracy improvement on IF-VR (Chen et al., 7 Aug 2025)).
Content Coverage and Cold-start Handling: LLM-augmented text and multi-modal fusion models (e.g., ViLLA-MMBench) achieve superior cold-start rates and catalog coverage, outperforming unimodal or ID-based baselines (Nazary et al., 6 Aug 2025).
Diversity and Multi-category Relevance: Techniques leveraging modularity, centrality, and coverage-oriented clustering deliver more diverse recommendations, sustaining long-term user engagement and fairness in content exposure (Maghsoudi et al., 2023, Liu et al., 2023).

6. Future Research and Open Challenges

Key directions identified through the lens of IF-VR benchmarks include:

Improved Clustering and Group Transfer: Exploration of clustering criteria, adaptive and reflective group formation, and hybrid group–individual reward schemes for further noise mitigation (Chen et al., 7 Aug 2025).
Rich Multi-modal Reasoning: Integration of richer visual, audio, behavioral, and user-context features, with the use of generative or causal modeling to explain skip/engage behaviors (Chen et al., 3 Jul 2025, Nazary et al., 6 Aug 2025).
Efficient Large-Scale Simulation: Model quantization, efficient user simulation, and scalable RL setups for real-time deployment in resource-constrained environments.
Transparent and Fair Benchmarks: Standardization of fairness, diversity, and novelty metrics; open-source, reproducible, and extensible IF-VR suites; and broader adoption in the research community (Nazary et al., 6 Aug 2025, Fang et al., 14 Aug 2024).
Deep Reasoning over Noisy Signals: Enhanced annotation and reward schemes for implicit feedback, and integration of user/group profiles to generate interpretable, robust RL policies for user simulation and future IF-VR extensions (Chen et al., 7 Aug 2025, Lin et al., 2023).

7. Comparative Table of Benchmarks and Methods

Benchmark/Method	Scale & Modalities	Core Innovations
IF-VR (Chen et al., 7 Aug 2025)	15k users, 25k videos,	Group-aware RL, LLM clustering,
	933k imp. interactions	multi-modal, skip reasoning
ViLLA-MMBench (Nazary et al., 6 Aug 2025)	MovieLens+MMTF-14K, audio/visual/text	LLM augmentation, modular fusion, YAML workflows
GRCN (Yinwei et al., 2021)	Movielens/Tiktok/Kwai, multimodal	Prototypical graph refinement, noise pruning
Kuaishou (Gong et al., 2022, Pan et al., 2023)	Billion-user industrial	On-device fast rerank, feedback-aware encoding, multi-objective training
DRGame (Liu et al., 2023)	Steam games, imbalanced	Balanced prefs, category clustering, asymmetric aggregation

Conclusion

IF-VR benchmarks have become the gold standard for robust, fair, and reproducible research in video recommendations under implicit feedback. By integrating systematic dataset design, multi-modal information, advanced context-aware denoising, simulation-based evaluation, and state-of-the-art performance and fairness metrics, IF-VR defines both the challenges and the methodological landscape for the next generation of video recommender systems in industrial and academic settings.