{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-deep-multimodal-approach-for-cold-start","title":"A Deep Multimodal Approach for Cold-start Music Recommendation","arxiv_id":"1706.09739","date":"2017-06-29","proceeding":null,"authors":["Sergio Oramas","Oriol Nieto","Mohamed Sordo","Xavier Serra"],"abstract":"An increasing amount of digital music is being published daily. Music\nstreaming services often ingest all available music, but this poses a\nchallenge: how to recommend new artists for which prior knowledge is scarce? In\nthis work we aim to address this so-called cold-start problem by combining text\nand audio information with user feedback data using deep network architectures.\nOur method is divided into three steps. First, artist embeddings are learned\nfrom biographies by combining semantics, text features, and aggregated usage\ndata. Second, track embeddings are learned from the audio signal and available\nfeedback data. Finally, artist and track embeddings are combined in a\nmultimodal network. Results suggest that both splitting the recommendation\nproblem between feature levels (i.e., artist metadata and audio track), and\nmerging feature embeddings in a multimodal approach improve the accuracy of the\nrecommendations.","url_abs":"http://arxiv.org/abs/1706.09739v2","url_pdf":"http://arxiv.org/pdf/1706.09739v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-deep-multimodal-approach-for-cold-start","repo_url":"https://github.com/sergiooramas/tartarus","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"music-recommendation","task_name":"Music Recommendation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.09739","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.09739"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sergiooramas/tartarus","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"92abb211babb78ac","entry":"load_sparse_csr","repo":"sergiooramas/tartarus","repo_kind":"official","path":"src/predict.py","file_url":"https://github.com/sergiooramas/tartarus/blob/HEAD/src/predict.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"92abb211babb78ac"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}