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3 results for “collaborative filtering”
Data associated with "A collaborative filtering based approach to biomedical knowledge discovery"
<p>This is the data set associated with the publication: "A collaborative filtering based approach to biomedical knowledge discovery" published in Bioinformatics.</p> <p>The data are sets of cooccurrences of biomedical terms extracted from published abstracts and full text articles. The cooccurrences are then represented in sparse matrix form. There are three different splits of this data denoted by the prefix number on the files.</p> <p>1. All - All cooccurrences combined in a single file</p> <p>2. Training/Validation - All cooccurrences in publications before 2010 in training, all novel cooccurrences in publication in 2010 go in validation</p> <p>3. Training+Validation/Test - All cooccurrences in publication upto and including 2010 in training+validation. All novel cooccurrences after 2010 in year by year increments and also all combined together</p> <p> </p> <p>Furthermore there are subset files which are used in some experiments to deal with the computational cost of evaluating the full set. The associated cuids.txt file containing a link between the row/column in the matrix with the UMLS Metathesaurus CUIDs. Hence the first row of cuids.txt matches up to the 0th row/column in the matrix. Note that the matrix is square and symmetric. This work was done with UMLS Metathesaurus 2016AB.</p>
Fair Graph Augmentation for Graph Collaborative Filtering
<p>Dataset for the paper submission `Fair Graph Augmentation for Graph Collaborative Filtering`. The included datasets are Foursquare New York City (FNYC), Foursquare Tokyo (FKTY), MovieLens 1M (ML1M), Last.FM 1M (LF1M), Rent The Runway (RENT)</p>
Files for Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation
<p>This are the files needed for running the experiments of "Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation".</p> <ul> <li> <p>listening_events.tsv.bz2 : Dataset excerpt from <a href="http://www.cp.jku.at/datasets/LFM-2b/">LFM-2b</a>, before filtering (see submission for details)</p> </li> <li> <p>BPR_item_embeddings.tsv.bz2 : Item embeddings obtained from the pre-trained BPR instance</p> </li> <li> <p>user_split.tar.bz2 : csv file of the listening history of each user</p> </li> <li> <p>2023_recsys_actr_poster.pdf ; poster presented at RecSys 2023</p> </li> </ul> <p>The code for running the experiments is available on <a href="https://github.com/hcai-mms/actr">GitHub</a><br> <br> If you use these files, please cite<br> </p> <blockquote> <p>@inproceedings{10.1145/3604915.3608838,<br> author = {Moscati, Marta and Wallmann, Christian and Reiter-Haas, Markus and Kowald, Dominik and Lex, Elisabeth and Schedl, Markus},<br> title = {Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation},<br> year = {2023},<br> isbn = {9798400702419},<br> publisher = {Association for Computing Machinery},<br> address = {New York, NY, USA},<br> url = {https://doi.org/10.1145/3604915.3608838},<br> doi = {10.1145/3604915.3608838},<br> 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.},<br> booktitle = {Proceedings of the 17th ACM Conference on Recommender Systems},<br> pages = {840–847},<br> numpages = {8},<br> keywords = {Music Recommender Systems, Psychology-Informed Recommender Systems, Collaborative Filtering, Adaptive Control Thought-Rational (ACT-R), Sequential Recommendation, Explainability},<br> location = {Singapore, Singapore},<br> series = {RecSys '23}<br> }</p> </blockquote> <p>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.</p>
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