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#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems
- 1. #nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems ASMITA PODDAR EVA ZANGERLE YI-HSUAN YANG National Institute of University of Academia Sinica, Technology, Rourkela, India Innsbruck, Austria Taiwan 05/07/2018 Asmita Poddar
- 2. Motivation Twitter and Spotify are ideal for information retrieval and analysis We need a consolidated dataset containing different data types like item content features, user contexts, as well as timestamps of the user-item association. Develop an integrated system to jointly model different data types relevant to different kinds of recommendation. 05/07/2018 Asmita Poddar
- 3. Outline Related datasets About #nowplaying-RS • What? • How? • Statistics Recommendation tasks • Methods • Evaluation metric • Baseline Results 05/07/2018 Asmita Poddar
- 4. Related Datasets for Music Recommendation 05/07/2018 Asmita Poddar
- 5. The Dataset The #Nowplaying-RS dataset consists of 11.6 million Listening Events (LEs) including: 140K Users 350K Tracks 45K Artists 45K Hashtags 5290 Sentiments 05/07/2018 Asmita Poddar
- 8. Distribution of Tweets Over Time Poor speaking is a reaction to fear. Presentations are not journal articles. They’re a completely different communication, and they require different skills. 05/07/2018 Asmita Poddar Red bars represent the number of unique users Blue bars represent the number of tweets.
- 9. Histogram showing the variation of average sentiment scores of the hashtags in the dataset. The sentiment sores have been scaled in the range of [0,1]. 05/07/2018 Asmita Poddar Affective Information
- 10. General LEs 05/07/2018 Asmita Poddar Histograms of Median Time Distances Per User LEs with affect-related hashtags
- 11. The Experiments Context-aware Recommendation 05/07/2018 Asmita Poddar Context-aware Next-song Recommendation
- 13. Context-aware Next-Song Recommendation Sequence of songs listened to by the user can be created from the timestamps Mean length of sequences per user:123 Median length of sequences per user: 13 We use previous-”N” songs listened to by a user to create recommendations 05/07/2018 Asmita Poddar
- 14. Creation of Training and Test Sets 05/07/2018 Asmita Poddar
- 16. Method 05/07/2018 Asmita Poddar Factorization Machines Supervised learning model They model interactions of variables by mapping them (the interactions) to a low dimensional space [ Steffen Rendle. 2012. Factorization Machines with libFM. ACM Trans. Intell. Syst. Technol. 3, 3, Article 57 (May 2012), 22 pages. DOI= http://dx.doi.org/10.1145/2168752.2168771 ]
- 17. Evaluation Metric 05/07/2018 Asmita Poddar Mean Reciprocal Rank (MRR)
- 19. Conclusion 05/07/2018 Asmita Poddar We present the #nowplaying-RS dataset, which can be used to compare and evaluate large-scale recommendation approaches in a real-life setting. We demonstrate various features of the dataset. We showcase two possible use cases of the dataset in our paper.
- 20. Future work 05/07/2018 Asmita Poddar Use the combination of mood-related hashtags and timestamps to track how people use music to modulate their emotion. Explore the scope of using the various features of the dataset to improve music recommendation quality using neural networks. Perform session-based music recommendation.
- 21. Links and Contact Info 05/07/2018 Asmita Poddar Link to dataset: http://dbis-nowplaying.uibk.ac.at/#nowplayingrs Link to implementation code: https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM : asmita_poddar : asmita.poddar@gmail.com : https://sites.google.com/view/asmitapoddar
- 22. Thank You! 05/07/2018 Asmita Poddar
Notas del editor
- Asmita – NITR At Taiwan TIGP Scholarship internship
- Who cares? Spotify, youtube Smart applications Twitter: 336 mio (11% of internet connected people), Spotify: 160 mio (5%) Users of music streaming services are becoming more particular about the songs they want to listen – depending on their mood, activity, time of day etc. Hence, streaming companies like Pandora, Spotify, to stay more competitive in todays’s market have to provide music recommendations which are relevant to a user. To do so, the best way is to train your RS based on context of the user, ie, context-aware music recommendation. Next song generation, sequential RS, session based RS, playlist generation etc. are recommendation tasks. To develop algos and do research for these for these tasks, we need a large scale dataset which will provide the necessary data. Address different types of recommendation like context-based, sequence-based/ next-song or session-based recommendation. it is a largescale, context-aware dataset; ii) the dataset contains timestamps for LEs, for sequence-based recommendation; iii) the hashtags contained are a unique feature : give an idea of the listening context and emotional state of the user at time of listening to track; for sentiment-aware approaches to recommendation and retrieval; iv) contains item content features of the songs v) it facilitates the development of an integrated system that jointly models different data types relevant to recommendation Develop algos for music recommendation for personalise music streaming, smart cars, smart speakers etc.
- Before talking about the details of #nowplaying-RS, I want to give an overview of the other music datasets that can be used for music recommendation. (Be sure of the dates and references of these datasets). Uniqueness: First, we provide a publicly available and extensive dataset of LEs, particularly suited for (sequential) context-aware recommendations. Second, we provide a great variety of context and content information about the users and tracks, as well as clues of the underlying emotions, activities, and thoughts of the users through hashtags.
- Sentiments are those sentiment scores of hashtags, which can be resolved using at least one of the 5 sentiment dictionaries: AFINN [26], Opinion Lexicon [27], SentiStrength [28], Vader [29] and the Sentiment Hashtag lexicon
- Barplot depicting the weekly distribution of the number of tweets (LEs) and number of users tweeting over time. Each bar represents the span of a week. Red bars represent the number of unique users and blue bars represent the number of tweets. This has been used to split the dataset into training and test sets.
- Lot od contextual information can be retrieved from the hashtags as can be seen from the examples. The sentiment information can give us valuable intelligence about the moods of the user. We get a wide range of mood information from the dataset. We can derive the change in mood of the user over time.
- This information is useful while making sequence based reco. We see that there are larger number of users with LEs having a short time span (50% of users have a time span of 0–37 hours) between successive LEs in general. About 50% of users who have used affect-related hashtags with LEs have a time span of 0–52 hours between successive LEs. This allows us to model user preferences or mood on a nearly daily basis, making this dataset useful for sequence-based recommendation.
- For our experiments, we took into account the user’s most recent song preference into consideration by taking N = 1, i.e., the “last song” that a user had listened to is used as the context information to infer the ”next song” that a user would likely listen to.
- The resulting set of 10 tracks can be subsequently used as input to an RS. Concretely, it can be understood that track recommendation under the POP USER population method is more difficult, because all the 10 candidate tracks are known to the user and likely the recommender has to rely on contextual features to pick the right one.
- Work well with sparse datasets. RS usually creates a sparse matrix
- Higher the MRR, better the recommendation
- Evaluation results in mean reciprocal rank (MRR) for (a) context-aware recommendation for the POP RND and POP USER settings and (b) context-aware next song recommendation for N = 1, using the POP RND setting. ‘CB’ and ‘Cx’ indicate whether the method is content-based or context-based, respectively. [Base] indicates a part of the input to FM. For (a) [Base] = User ID+Track ID and (b) [Base] = User ID+Track ID+Previous Track ID. We highlight the top two results per column using bold font.