However, none of the existing methods consider all these three aspects, leading to a suboptimal tag recommendation. In this paper, we identify three pillars that impact the accuracy of tag recommendation: (1) sequential text modeling meaning that the intrinsic sequential ordering as well as different areas of text might have an important implication on the corresponding tag(s), (2) tag correlation meaning that the tags for a certain piece of textual content are often semantically correlated with each other, and (3) content-tag overlapping meaning that the vocabularies of content and tags are overlapped. Recommending suitable tags for online textual content is a key building block for better content organization and consumption. Our results indicate that our proposed method is both effective for recommending attribute-diverse relevant tags and efficient at scalable processing. We evaluate our tag recommendation system on CAL-500 and a large-scale data set ($N = 77,448$ songs) generated by crawling Youtube and Last.fm. For processing large-scale music data sets, we design parallel algorithms based on the MapReduce framework to perform large-scale music content and social tag analysis, train a model, and compute tag similarity. Our system is designed for large-scale deployment, on the order of millions of objects. To the best of our knowledge, this is the first method to consider Explicit Multiple Attributes for tag recommendation. Once the user uploads or browses a song, the system recommends a list of relevant tags in each attribute independently. In our approach, the attribute space is explicitly constrained at the outset to a set that minimizes semantic loss and tag noise, while ensuring attribute diversity. We propose a scheme for tag recommendation using Explicit Multiple Attributes based on tag semantic similarity and music content.
Many of these are underrepresented by current tag recommenders. Music attributes encompass any number of perceived dimensions, for instance vocalness, genre, and instrumentation. However, current methods do not consider diversity of music attributes, often using simple heuristics such as tag frequency for filtering out irrelevant tags. Towards addressing these shortcomings, tag recommendation for more robust music discovery is an emerging topic of significance for researchers. Such collaborative intelligence, however, also generates a high degree of tags unhelpful to discovery, some of which obfuscate critical information. Social tagging can provide rich semantic information for large-scale retrieval in music discovery.
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