This are the files needed for running the experiments of "Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation". listening_events.tsv.bz2 : Dataset excerpt from LFM-2b, before filtering (see submission for details) BPR_item_embeddings.tsv.bz2 : Item embeddings obtained from the pre-trained BPR instance user_split.tar.bz2 : csv file of the listening history of each user 2023_recsys_actr_poster.pdf ; poster presented at RecSys 2023 The code for running the experiments is available on GitHub If you use these files, please cite @inproceedings{10.1145/3604915.3608838, author = {Moscati, Marta and Wallmann, Christian and Reiter-Haas, Markus and Kowald, Dominik and Lex, Elisabeth and Schedl, Markus}, title = {Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation}, year = {2023}, isbn = {9798400702419}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3604915.3608838}, doi = {10.1145/3604915.3608838}, abstract = {Music listening sessions often consist of sequences including repeating tracks. Modeling such relistening behavior with models of human memory has been proven effective in predicting the next track of a session. However, these models intrinsically lack the capability of recommending novel tracks that the target user has not listened to in the past. Collaborative filtering strategies, on the contrary, provide novel recommendations by leveraging past collective behaviors but are often limited in their ability to provide explanations. To narrow this gap, we propose four hybrid algorithms that integrate collaborative filtering with the cognitive architecture ACT-R. We compare their performance in terms of accuracy, novelty, diversity, and popularity bias, to baselines of different types, including pure ACT-R, kNN-based, and neural-networks-based approaches. We show that the proposed algorithms are able to achieve the best performances in terms of novelty and diversity, and simultaneously achieve a higher accuracy of recommendation with respect to pure ACT-R models. Furthermore, we illustrate how the proposed models can provide explainable recommendations.}, booktitle = {Proceedings of the 17th ACM Conference on Recommender Systems}, pages = {840–847}, numpages = {8}, keywords = {Music Recommender Systems, Psychology-Informed Recommender Systems, Collaborative Filtering, Adaptive Control Thought-Rational (ACT-R), Sequential Recommendation, Explainability}, location = {Singapore, Singapore}, series = {RecSys '23} } This research was funded in whole, or in part, by the Austrian Science Funds (FWF): P33526 and DFH-23, and by the State of Upper Austria and the Federal Ministry of Education, Science, and Research, through grant LIT-2020-9-SEE-113.