Context-aware music recommendation based on latenttopic sequential patterns

By N. Hariri, Bamshad Mobasher, Robin D. Burke. ACM Conference on Recommender Systems, 2012

314 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/47ab879139457c9cadf25ca37f00bc5fa9b4c453

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dietmar Jannach
2. Malte Ludewig
3. Paolo Cremonesi
4. Longbing Cao
5. Quan Z. Sheng
6. Shoujin Wang
7. Yan Wang
8. Chang Zhou

In Information Systems

1. Dietmar Jannach
2. Malte Ludewig
3. Longbing Cao
4. Yan Wang
5. Quan Z. Sheng
6. Shoujin Wang
7. Paolo Cremonesi
8. Massimo Quadrana

In Signal Processing

1. Chang Zhou
2. Dietmar Jannach
3. Paolo Cremonesi
4. Geoffray Bonnin
5. Massimo Quadrana
6. Longbing Cao
7. Malte Ludewig

Citations in the corpus

• Song recommendation with non-negative matrix factorization and graph total variation (2016, unknown): “We define matrix C ∈ {0, 1}n×m as in [3,13], that has a value Cij = 1 if playlist i contains song j, 0 otherwise.”
• A Survey on Session-based Recommender Systems (2019, background): “Representative studies include music recommendation based on latent topic sequential patterns [34], and playlist generation with statistical models on music-listening sessions [149].”
• Music Recommendation (2016, background): “Among the recent work, [15] proposed a frequent pattern mining approach where patterns of latent topics are extracted from user playlists and then used to compute recommendations.”
• ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation (2017, methodology): “RNN based methods are studied for recommendation in academic fields in recent years, which builds the recommendation context directly from the behaviors for each user (Rendle et al. 2011; Hariri, Mobasher, and Burke 2012; Wu et al. 2016).”
• Sequence-Aware Recommender Systems (2018, methodology): “LDA/Clustering w/ sequence learning [40, 87, 99]”
• When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation (2017, background): “ing sequences of topics was also the goal in [11] for the next-track music recommendation scenario.”
• Evaluation of session-based recommendation algorithms (2018, methodology): “, meta-data or content features) about the items of the session, as was done, for example, in [Hariri et al. 2012] or [Hidasi et al.”