Download Music Recommendation and Discovery: The Long Tail, Long by Òscar Celma PDF

By Òscar Celma

With a lot more tune to be had nowadays, conventional methods of discovering tune have lowered. at the present time radio exhibits are usually programmed through huge organizations that create playlists drawn from a restricted pool of tracks. equally, checklist shops were changed by means of big-box shops that experience ever-shrinking track departments. rather than counting on DJs, record-store clerks or their buddies for track concepts, listeners are turning to machines to steer them to new music.

In this ebook, Òscar Celma publications us during the global of computerized song suggestion. He describes how track recommenders paintings, explores many of the obstacles noticeable in present recommenders, deals recommendations for comparing the effectiveness of tune thoughts and demonstrates tips on how to construct potent recommenders by means of providing real-world recommender examples. He emphasizes the user's perceived caliber, instead of the system's predictive accuracy while offering techniques, hence permitting clients to find new track via exploiting the lengthy tail of recognition and selling novel and suitable fabric ("non-obvious recommendations"). that allows you to achieve out into the lengthy tail, he must weave ideas from complicated community research and track details retrieval.

Aimed at final-year-undergraduate and graduate scholars engaged on recommender platforms or song info retrieval, this ebook provides the state-of-the-art of the entire various concepts used to suggest goods, concentrating on the tune area because the underlying application.

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Some of the methods that Burke defines are [29]: • Weighted. A hybrid method that combines the output of separate approaches using, for instance, a linear combination of the scores of each recommendation technique. • Switching. The system uses some criterion to switch between recommendation techniques. One possible solution is that the system uses a technique, and if the results are not confident enough, it switches to another technique to improve the recommendation process. • Mixed. In this approach, the recommender does not combine but expand the description of the data sets by taking into account the users’ ratings and the description of the items.

Actually, giving explanations about why the items were recommended is as important as the actual list of recommended items. Tintarev and Masthoff summarise the possible aims for providing recommendations. These are: transparency, scrutability, trust, effectiveness, persuasiveness, efficiency, and satisfaction. They also stress the importance of personalising the explanations to the user [34]. 3 Cold Start Problem As already mentioned, the cold start problem of a recommender (also known as the learning rate curve, or the bottleneck problem) happens when a new user (or a new item) enters into the system [35].

Schmidt-Thieme, “Tag-aware recommender systems by fusion of collaborative filtering algorithms,” in Proceedings of the ACM Symposium on Applied Computing, (New York, NY), pp. 1995–1999, ACM, 2008. 26. M. Levy and M. Sandler, “A semantic space for music derived from social tags,” in Proceedings of the 8th International Conference on Music Information Retrieval, (Vienna, Austria), 2007. 27. P. Symeonidis, M. Ruxanda, A. Nanopoulos, and Y. Manolopoulos, “Ternary semantic analysis of social tags for personalized music recommendation,” in Proceedings of 9th International Conference on Music Information Retrieval, (Philadelphia, PA), 2008.

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