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We present a hybrid framework that uses large language models (LLMs) to construct and enrich a domain-specific knowledge graph (KG) from item-side text, and then injects the resulting semantic structure into downstream recommendation models. The proposed pipeline follows a two-phase design. In Phase\u00a01, we induce a movie-domain KG from MovieLens metadata and textual signals, align it to external resources, and evaluate KG quality through complementary intrinsic strategies covering exact matching, structural diagnostics, and embedding-based semantic similarity. In Phase\u00a02, we learn KG-derived embeddings (including translational and bilinear variants) and integrate them into a broad set of state-of-the-art recommenders spanning collaborative filtering, feature-interaction models, and graph-based models. Across models and metrics, KG-augmented representations yield consistent improvements in top-\n                    <jats:italic>K<\/jats:italic>\n                    ranking quality (e.g., Recall@\n                    <jats:italic>K<\/jats:italic>\n                    , NDCG@\n                    <jats:italic>K<\/jats:italic>\n                    , Precision@\n                    <jats:italic>K<\/jats:italic>\n                    ). Beyond end-to-end accuracy, we analyze how intrinsic KG quality signals relate to downstream recommendation gains, and we distill practical guidance on when LLM-generated KGs are most beneficial. Overall, this provides a reusable pipeline and empirical evidence that coupling LLM-based knowledge induction with structured KG reasoning can improve both recommendation effectiveness and interpretability.\n                  <\/jats:p>","DOI":"10.1007\/s44163-026-01404-y","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T14:53:06Z","timestamp":1779375186000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large language model enhanced embeddings for knowledge aware recommender systems"],"prefix":"10.1007","volume":"6","author":[{"given":"Het Darshan","family":"Mehta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Polignano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Semeraro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ernesto William","family":"De Luca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"1404_CR1","doi-asserted-by":"publisher","unstructured":"Ricci F, Rokach L, Shapira B, Recommender Systems Handbook 2nd edn (Springer, 2015). https:\/\/doi.org\/10.1007\/978-1-0716-2197-4.","DOI":"10.1007\/978-1-0716-2197-4"},{"key":"1404_CR2","doi-asserted-by":"publisher","unstructured":"Zhang S, Yao L, Sun A, Tay Y, Deep Learning based recommender system: a survey and new perspectives. 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