VideoRec Models: End-to-End Video Recommendations

• VideoRec models are deep learning architectures that use trainable video encoders to learn end-to-end from raw video modalities for personalized recommendations.
• They outperform traditional ID and VIDRec systems by effectively capturing temporal, visual, and auditory cues, leading to improved HR@10 and NDCG@10 metrics.
• Practical insights include the advantages of sequential modeling, efficient fine-tuning strategies, and incorporating generative priors to enhance robustness in micro-video recommendations.

VideoRec models constitute a class of deep learning architectures that perform end-to-end recommendation on video-oriented platforms by directly learning from raw video modalities. By integrating video understanding and recommendation, these models jointly optimize user preference predictors with trainable video encoders, thereby surpassing traditional ID-based approaches that rely strictly on user and item IDs or frozen, pre-extracted features. VideoRec models are a central focus in large-scale micro-video recommendation benchmarks and serve as a template for unifying vision and recommendation methodologies.

1. Conceptual Foundations and Motivation

Traditional recommender systems for video content often operate in one of two paradigms: (1) utilizing only user/item IDs with collaborative filtering (CF), or (2) leveraging frozen video features extracted from pre-trained encoders (“VIDRec”) (Ni et al., 2023). These strategies typically underutilize the fine-grained temporal, visual, and auditory structure present in video data. VideoRec models address these deficits by replacing fixed video representations with learnable, end-to-end video encoders ($f_\theta$) that are simultaneously trained with the recommender objective, enabling the extraction of video features directly optimized for the recommendation task.

This framework is particularly critical as micro-video platforms (e.g., short-form video applications) deliver content with complex multimodal signals (video frames, audio, text, images). The hypothesis underlying VideoRec is that exposing the recommender to raw modalities, and fine-tuning the encoder on actual user-item interaction data, yields representations much more predictive of downstream engagement and preference than off-the-shelf video classification features.

2. Architectural Structure and Variants

VideoRec systems instantiate the following architectural principles (Ni et al., 2023):

• The item (video) representation is computed by a deep video encoder $f_\theta(\cdot)$, such as SlowFast, applied directly to sampled video frames and optionally fused with additional modalities (audio, text).
• The user representation $p_u$ is produced via lookup (identity) or, in the sequential variant, by a sequence encoder (e.g., SASRec, NextItNet, GRU4Rec) over the user’s interaction history.
• Prediction is made via a bilinear or dot-product suitability score: $\hat y_{u,v} = \sigma(p_u^\top h_v)$, where $h_v$ is the learned video representation.

Three broad families of models in published benchmarks are:

• IDRec: Content-agnostic, pure ID embedding.
• VIDRec: ID plus frozen video features, with $h_v$ derived from an external, pre-trained encoder (e.g., VideoMAE).
• VideoRec: End-to-end, video modality-driven, with $h_v=f_\theta($video frames$)$ learned jointly via recommendation loss.

The training objective is an in-batch softmax cross-entropy loss, typically of the form:

$$\mathcal L = -\sum_{u}\log\frac{\exp(p_u^\top q_{v^+}/\tau)}{\sum_{v\in \mathcal B}\exp(p_u^\top q_v/\tau)}$$

where the positive video $v^+$ corresponds to the observed interaction and $\tau$ is a temperature parameter.

3. Evaluation Metrics and Experimental Protocols

Performance is evaluated with standard leave-one-out protocols and metrics at cutoff $K$, predominantly Hit Ratio@K ($\mathrm{HR@K}$) and normalized discounted cumulative gain ($\mathrm{NDCG@K}$):

• $$\mathrm{HR@K} = \frac{1}{|U|}\sum_{u} \mathbb I\{v_{\rm heldout}\in \text{top-}K(u)\}$$
• $$\mathrm{NDCG@K} = \frac{1}{|U|}\sum_u \frac{\mathbb I\{v_{\rm heldout}=v_i\}}{\log_2(i+1)}$$

These metrics assess the model’s ability to rank held-out items for each user highly, reflecting both recall-type performance and early relevance in recommendation lists.

In large-scale benchmarks such as “MicroLens,” VideoRec models—especially sequential variants using architectures like SASRec—exhibit consistent outperformance over IDRec and VIDRec, attaining the highest HR/NDCG scores (Ni et al., 2023).

Model	HR@10	NDCG@10
SASRec (IDRec SR)	0.0909	0.0517
SASRec_V (VideoRec)	0.0948	0.0515
GRU4Rec_V (VideoRec)	0.0954	0.0517
NextItNet_V (VideoRec)	0.0862	0.0466

4. Empirical Findings and Key Insights

Empirical analyses reveal several recurring patterns (Ni et al., 2023):

• Sequential modeling (using temporal user history) yields relative improvements exceeding 100% over collaborative filtering baselines, underscoring the importance of personalized, sequence-aware representations.
• Frozen video features added as side information (“VIDRec”) rarely improve recommendation compared to pure ID-based models. In many cases, they degrade performance, suggesting that off-the-shelf video classifiers do not yield features aligned with engagement prediction.
• End-to-end VideoRec provides absolute gains of 5–10% in HR@10 over both IDRec and VIDRec, confirming the necessity of jointly learning video representations with user feedback.
• Fine-tuning strategies: Updating only the top layers (“topT”) of the video encoder is nearly as effective as full fine-tuning (“FT”), offering computational efficiency and mitigating catastrophic forgetting.
• Modality ablation: Using cover images alone instead of full video data results in a 3–4% drop in HR@10, indicating the critical role of temporal and auditory information. The video encoder must, therefore, effectively capture spatiotemporal and multimodal signals.
• Universal representations: E2E VideoRec models can outperform ID-based approaches even in extreme cold- and warm-start scenarios, suggesting the learned video features are broadly transferable.

5. Extensions to Generative-Discriminative Video Learning

Recent research explores the intersection of generative modeling and recognition, where video diffusion models are jointly trained for both generation and discrimination tasks. GenRec exemplifies a joint approach, utilizing random-frame–conditioned video diffusion to produce spatial-temporal features robust to missing or partial observations (Weng et al., 2024). The recognition head is integrated into a denoising UNet backbone, and the model is jointly optimized using both a diffusion loss and a cross-entropy recognition loss:

$L = L_{\rm diff} + \lambda L_{\rm rec}$

A key insight is that the generative prior (via noisy reconstruction tasks) forces the model to extract robust, generalizable features, improving action recognition—particularly under sparse or early-frame regimes. Empirical results indicate top-1 accuracy of 75.8% on SSV2 and 87.2% on Kinetics-400, with exceptional robustness when only a subset of frames is observed. Ablation studies confirm that omitting the generative loss leads to a 2–3% drop in recognition under sparse inputs (Weng et al., 2024).

6. Application in Bandwidth-Constrained Video Recognition

QRMODA and BRMODA introduce closed-form models for predicting face-recognition recall error as a function of video encoding parameters: spatial resolution, quantization parameter ($Q_p$), and bitrate ($R$) (Hamandi et al., 2019). These models are not VideoRec models per se, but offer practical value for real-world video recommendation and recognition systems operating under network constraints.

• QRMODA: Models recall error as a shifted-logistic in $Q_p$ whose midpoint scales with resolution.
• BRMODA: Models error as the sum of two negative exponentials in $R$, parameterized by resolution.

When calibrated per deployment scenario, these models guide real-time control policies that adapt quality settings (resolution, $Q_p$, $R$) to maintain recognition accuracy above a defined threshold.

Calibration on datasets such as Honda/UCSD and DISFA yields $R^2 > 0.98$ fits for recall (and related metrics), demonstrating their robustness for both neural and classical recognition pipelines.

7. Implications and Future Directions

The empirical body of work demonstrates that VideoRec models—by integrating video representation learning and recommendation—set a new standard for content-driven, multimodal ranking systems. The evidence that off-the-shelf frozen features are suboptimal for engagement prediction underscores the importance of fine-tuning encoders on behavioral feedback rather than relying on generic video understanding metrics. Furthermore, the seamless integration of generative priors (via diffusion modeling) with recognition heads signals a trend toward unified, multi-purpose video models capable of robust discrimination, generation, and adaptation to practical deployment constraints. This motivates further research into transferable, modality-agnostic video encoders and cross-domain adaptation in large-scale recommendation settings (Ni et al., 2023, Weng et al., 2024, Hamandi et al., 2019).