![]() Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. In: Proceedings of the Eighth International Conference on Hybrid Intelligent Systems (HIS 2008), pp. In: 7th ACM Conference on Recommender Systems Workshop on Crowdsourcing and Human Computation for Recommender Systems (RecSys 2013) (2013)įong, S., Ho, Y., Hang, Y.: Using genetic algorithm for hybrid modes of collaborative filtering in online recommenders. Springer (2010)ĭooms, S., De Pessemier, T., Martens, L.: Movietweetings: a movie rating dataset collected from twitter. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)Ĭelma, Ò.: Music Recommendation and Discovery - The Long Tail, Long Fail, and Long Play in the Digital Music Space. University of Miami (2011)īurke, R.: Hybrid recommender systems: Survey and experiments. ![]() In: Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), pp. Hyperion (2006)īertin-Mahieux, T., Ellis, D.P.W., Whitman, B., Lamere, P.: The million song dataset. KeywordsĪnderson, C.: The Long Tail: Why the Future of Business Is Selling Less of More. We show that the performance of our proposed recommender is promising and clearly outperforms the baseline. In the evaluation part of this work, we benchmark the presented recommender system against two baseline approaches. As this dataset is updated daily, we propose a genetic algorithm, which allows the recommender system to adopt its input parameters to the extended dataset. In order to overcome this problem, we present a music recommendation system exploiting a dataset containing listening histories of users, who posted what they are listening to at the moment on the microblogging platform Twitter. Besides focusing on the computation of the recommendations itself, in literature the problem of a lack of data appropriate for research is discussed. For helping customers finding products according to their taste on those platforms, recommender systems play an important role. Personally, even as someone who doesn’t currently use Spotify’s social features, I think this looks like a fantastic idea.The rise of the web enabled new distribution channels like online stores and streaming platforms, offering a vast amount of different products. On top of that, you can add select friends to the Friends Weekly list to have their activity influence the playlist, and you can even react to their songs with emojis. Spotify hasn’t provided any official information on this one, but leaked screenshots reveal that this feature would have a Snapchat story-like UI which lets you see what your friends are listening to and even sync up with their queue. The most exciting thing Spotify is testing out on Android at the moment is a new featured called “Friends Weekly.” This feature builds on the popular Discover Weekly feature by creating a personalized weekly playlist based on what your friends are listening to. ![]() Now, the popular app is testing out a couple of big changes on its Android app. While Google may be trying to up its game with YouTube Music, Spotify is still one of the most popular services out there today for streaming music.
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