Recommendation Systems

SemOpenAlex is an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts. * SemOpenAlex is licensed under CC0, providing free and open access to the data. * We offer the data through multiple channels, including RDF dump files, a SPARQL endpoint, and as a data source in the Linked Open Data cloud, complete with resolvable URIs and links to other data sources (ISNI, DOI, ORCID, ROR, Scopus, DOAJ, Wikidata, * Moreover, we provide embeddings for knowledge graph entities using high-performance computing. SemOpenAlex enables a broad range of use-case scenarios, such as * exploratory semantic search via our website, * large-scale scientific impact quantification, * other forms of scholarly big data analytics within and across scientific disciplines. * enables academic recommender systems, such as recommending collaborators, publications, and venues, including explainability capabilities. * can serve for RDF query optimization benchmarks, * creating scholarly knowledge-guided language models, * as a hub for semantic scientific publishing.

A set of approximately 100K podcast episodes comprised of raw audio files along with accompanying ASR transcripts. This represents over 47,000 hours of transcribed audio, and is an order of magnitude larger than previous speech-to-text corpora. Source: The Spotify Podcast Dataset

Coached Conversational Preference Elicitation is a dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers, where one worker plays the role of an 'assistant', while the other plays the role of a 'user'. Image Source: https://www.aclweb.org/anthology/W19-5941

The Epinions dataset is trust network dataset. For each user, it contains his profile, his ratings and his trust relations. For each rating, it has the product name and its category, the rating score, the time point when the rating is created, and the helpfulness of this rating. Source: https://www.cse.msu.edu/~tangjili/datasetcode/truststudy.htm

KG20C is a Knowledge Graph about high quality papers from 20 top computer science Conferences. It can serve as a standard benchmark dataset in scholarly data analysis for several tasks, including knowledge graph embedding, link prediction, recommendation systems, and question answering . For more information and download, please see the dataset homepage.

Amazon Fine Foods is a dataset that consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plaintext review. Source: https://snap.stanford.edu/data/web-FineFoods.html

This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

Publicly available dataset in the hotel domain (50M versus 0.9M) and additionally, the largest recommendation dataset in a single domain and with textual reviews (50M versus 22M). Source: HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset

We ran 21 recommender systems on three datasets (BeerAdvocate, LibraryThing and MovieLens 1M). The output of these recommenders was evaluated using rec_eval tool. We also measured statistically significant improvements using permutation test. The output of both tools can be found in data.

Dataset of restaurant reviews from TripAdvisor that includes images and texts uploaded in reviews by users. Reviews in six different cities are included: Gijón (Spain), Barcelona (Spain), Madrid (Spain), New York City (USA), Paris (France) and London (United Kingdom). In the original publication, the following task is proposed: Can we explain, using the existing image or text from a different user, why a given restaurant was recommended to a certain user?

xMIND is an open, large-scale multilingual news dataset for multi- and cross-lingual news recommendation. xMIND is derived from the English MIND dataset using open-source neural machine translation (i.e., NLLB 3.3B). xMIND contains 130K news translated into 14 linguistically and geographically diverse languages, with digital footprints of varying sizes. The goal of xMIND is to serve as a benchmark dataset for news recommendation, and to foster broader research into multilingual and cross-lingual news recommendation, for speakers of both high and low-resource languages.

Delicious : This data set contains tagged web pages retrieved from the website delicious.com. Source: Text segmentation on multilabel documents: A distant-supervised approach Image Source: http://mlkd.csd.auth.gr/multilabel.html

E-ReDial is a conversational recommender system dataset with high-quality explanations. It consists of 756 dialogues with 12,003 utterances, each with 15.9 turns on average. 2,058 high-quality explanations are included, each with 79.2 tokens on average.

Data from the popular Chinese online shopping platform Taobao includes behaviors like buy, add-to-cart, add-to-favorite, and pageview. The buying behavior is considered the target behavior.

A dataset consisting of recipient 46 users and, 26180 tweets. The dataset includes the news feed of the users and 13 features that may influence the relevance of the tweets.

The dataset consists of three files: a file with behaviour data (events.csv), a file with item properties (itemproperties.сsv) and a file, which describes category tree (categorytree.сsv). The data has been collected from a real-world ecommerce website. It is raw data, i.e. without any content transformations, however, all values are hashed due to confidential issues. The purpose of publishing is to motivate researches in the field of recommender systems with implicit feedback.

Uses a platform with 77 candies and sweets to rank. Over 2000 users submitted over 44000 grades resulting in a matrix with 28% coverage. Source: SweetRS: Dataset for a recommender systems of sweets

Wikidata-14M is a recommender system dataset for recommending items to Wikidata editors. It consists of 220,000 editors responsible for 14 million interactions with 4 million items.

Wyze Rule Recommendation Dataset. It is a big dataset with 300,000 users. Please cite [1] if you used the dataset and cite [2] if you referenced the algorithm. [1] Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking. NeurIPS Dataset Track 2023 [2] FedRule: Federated Rule Recommendation System with Graph Neural Networks. IoTDI 2023

The CAL10K dataset (introduced as Swat10k) contains 10,870 songs that are weakly-labelled using a tag vocabulary of 475 acoustic tags and 153 genre tags. The tags have all been harvested from Pandora’s website and result from song annotations performed by expert musicologists involved with the Music Genome Project. Source: Exploring automatic music annotation with “acoustically-objectiv” tags

Description This Dataset contains review information on Google map (ratings, text, images, etc.), business metadata (address, geographical info, descriptions, category information, price, open hours, and MISC info), and links (relative businesses) up to Sep 2021 in the United States. ## Citation Please cite the following papers if you use the data in any way: UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining Jiacheng Li, Jingbo Shang, Julian McAuley Annual Meeting of the Association for Computational Linguistics (ACL), 2022 Personalized Showcases: Generating Multi-Modal Explanations for Recommendations An Yan, Zhankui He, Jiacheng Li, Tianyang Zhang, Julian Mcauley The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023

This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices. The dataset accompanies the publication "System Design for a Data-driven and Explainable Customer Sentiment Monitor" (submitted). We provide an anonymized version of data collected over a period of two years. The dataset should fuel the research and development of new machine learning algorithms to better cope with real-world data challenges including sparse and noisy labels, and concept drifts. Additional challenges are the optimal fusion of enterprise and log-based features for the prediction task. Thereby, the interpretability of designed prediction models should be ensured in order to have practical relevancy. Supporting software Kindly use the corresponding GitHub repository (https://github.com/annguy/customer-sentiment-monitor) to design and benchmark your algorithms. Citation and Contact If you use this dataset please cite the following publication: @ARTICLE{9520354, author={Nguyen, An and Foerstel, Stefan and Kittler, Thomas and Kurzyukov, Andrey and Schwinn, Leo and Zanca, Dario and Hipp, Tobias and Jun, Sun Da and Schrapp, Michael and Rothgang, Eva and Eskofier, Bjoern}, journal={IEEE Access}, title={System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data}, year={2021}, volume={9}, number={}, pages={117140-117152}, doi={10.1109/ACCESS.2021.3106791}} If you would like to get in touch, please contact an.nguyen@fau.de.

A large scale, C2C marketplace e-commerce dataset.

Music recommendation for videos attracts growing interest in multi-modal research. However, existing systems focus primarily on content compatibility, often ignoring the users’ preferences. Their inability to interact with users for further refinements or to provide explanations leads to a less satisfying experience. We address these issues with MuseChat, a first-of-its-kind dialogue-based recommendation system that personalizes music suggestions for videos. Our system consists of two key functionalities with associated modules: recommendation and reasoning. The recommendation module takes a video along with optional information including previous suggested music and user’s preference as inputs and retrieves an appropriate music matching the context. The reasoning module, equipped with the power of Large Language Model (Vicuna-7B) and extended to multi-modal inputs, is able to provide reasonable explanation for the recommended music. To evaluate the effectiveness of MuseChat, we build a large-scale dataset, conversational music recommendation for videos, that simulates a two-turn interaction between a user and a recommender based on accurate music track information. Experiment results show that MuseChat achieves significant improvements over existing video-based music retrieval methods as well as offers strong interpretability and interactability. The dataset of this work is available at https://dongzhikang.github.io/musechat.

The OTTO session dataset is a large-scale dataset intended for multi-objective recommendation research. We collected the data from anonymized behavior logs of the OTTO webshop and the app. The mission of this dataset is to serve as a benchmark for session-based recommendations and foster research in the multi-objective and session-based recommender systems area. We also launched a Kaggle competition with the goal to predict clicks, cart additions, and orders based on previous events in a user session. For additional background, please see the published OTTO Recommender Systems Dataset GitHub. ## Key Features - 12M real-world anonymized user sessions - 220M events, consiting of clicks, carts and orders - 1.8M unique articles in the catalogue - Ready to use data in .jsonl format - Evaluation metrics for multi-objective optimization ## Dataset Statistics | Dataset | #sessions | #items | #events | #clicks | #carts | #orders | Density [%] | | :------ | ---------: | --------: | ----------: | ----------: | ---------: | --------: | ----------: | | Train | 12.899.779 | 1.855.603 | 216.716.096 | 194.720.954 | 16.896.191 | 5.098.951 | 0.0005 | | Test | 1.671.803 | 1.019.357 | 13.851.293 | 12.340.303 | 1.155.698 | 355.292 | 0.0005 |

an image cover dataset in short video recommendation

The datasets of "Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation" (TNNLs 2022)

We release a large-scale Multi-Scenario Multi-Modal CTR dataset named AntM2C, built from real industrial data from Alipay. This dataset offers an impressive breadth and depth of information, covering CTR data from four diverse business scenarios, including advertisements, consumer coupons, mini-programs, and videos. Unlike existing datasets, AntM2C provides not only ID-based features but also five textual features and one image feature for both users and items, supporting more delicate multi-modal CTR prediction. This dataset is from 110 million user exposure-click samples in 5 scenarios on the Alipay APP, including 670,000 users and 180,000 items, with 37 features. For more detailed information, please see the homepage: https://www.atecup.cn/dataSetDetailOpen/1

X-Wines is a consistent wine dataset containing 100,646 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1–5 ratings carried out over a period of 10 years (2012–2021) for wines produced in 62 different countries.