## 7 Discussion
### Comparative Analysis of Surveyed Works
#### 7.1.3 Paradigm Adoption Trends  
This survey reveals a growing shift from traditional discriminative pipelines (ranking, CTR prediction) to generative and hybrid paradigms. This change is driven by the need for adaptive, conversational, and narrative-aware recommendation engines.  
As seen in Figure 3,  
* Some 57% of papers use discriminative models (e.g., BERT4 Rec, PALR), primarily designed for structured prediction tasks such as next-item recommendation, click-through rate (CTR) estimation, and user-item ranking. These models typically rely on supervised learning with large labeled interaction datasets. Most employ fine-tuning on domain-specific corpora (e.g., Amazon, MIND) and achieve high performance on metrics like NDCG, Recall@K, and AUC. However, their reliance on labeled training data and limited capacity for dynamic reasoning restricts adaptability across domains.
* Some 35% leverage generative systems (e.g., GPT4 Rec, RecMind) to handle open-ended, narrative, and conversational recommendation tasks. These models support zero-shot and few-shot learning scenarios, making them particularly useful in domains with sparse supervision or cold-start users/items. Generative models are commonly evaluated using BLEU, ROUGE, and diversity scores in addition to traditional metrics. Their strengths lie in producing personalized explanations, summarizing user history, and engaging in real-time dialogue, which are particularly valuable in media, entertainment, and education use cases.
* Some 8% adopt hybrid frameworks that combine discriminative and generative reasoning (e.g., CoLLM, FLAN-Tuned Recs). These models often encode user-item interaction sequences using Transformers (e.g., BERT or XLNet), and then apply generative heads or prompt-based decoding layers to produce natural-language recommendations or justifications. Hybrid systems are also more likely to integrate multimodal data (e.g., visual content, reviews, metadata) and are evaluated with a combination of CTR, diversity, and human evaluation scores. They offer the best of both paradigms but introduce challenges in training pipeline complexity and latency.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline
**Domain** & **Percentage** & **Datasets and Notable Models** \\ \hline E-commerce & 32\% & Amazon, Taobao, AliExpress datasets’ use cases include cold-start prediction, multilingual personalization, and review-aware ranking (e.g., CoLLM, ChatGPT4 Rec). \\ \hline Healthcare & 18\% & ClinicalBERT, Synthea, ad HealthTweets focus on sensitive data handling, trust, and privacy-preserving recommendations. \\ \hline Media \& Entertainment & 16\% & Spotify, YouTube, MovieLens are applications in sequential and real-time personalization (e.g., RecMind, GPT4 Rec). \\ \hline Education & 12\% & EdNet, KDD Cup, and ASSISTments; LLMs model student behavior, adaptive learning, and knowledge tracing. \\ \hline Social Media & 11\% & Twitter, Reddit, and Instagram use LLMs for personalized content feeds, engagement prediction, and toxicity filtering. \\ \hline News \& Lifestyle & 11\% & MIND, Yahoo! News, HeartSteps; LLMs support context-aware, sentiment-driven, and multilingual recommendation tasks. \\ \hline \end{tabular}
\end{table}
Table 13: Domain -wise distribution of LLM4 Rec applications. Source: Author’s own design.