UniMobile: A Collection of Cross-App Mobile Search Queries

As the first step towards developing a unified search framework for mobile devices, the task of Target Apps Selection has been defined. To train and evaluate models for this task, a dataset with over 5000 queries has been built using crowdsourcing. This dataset is a result of a joint effort by researchers from the Università della Svizzera italiana (USI), Lugano, Switzerland and the University of Massachusetts, Amherst, MA, USA. To download the UniMobile dataset, please visit here. Citation:

  1. Target Apps Selection: Towards a Unified Search Framework for Mobile Devices Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W. Bruce Croft In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018 (SIGIR ’18) [Preprint] [Data]

Million Playlist Dataset

The ACM Recommender Systems Challenge 2018 focuses on a novel task in the field of recommender systems and information retrieval: Automatic Playlist Continuation. This year’s RecSys Challenge is organized by Spotify, University of Massachusetts Amherst, and Johannes Kepler University Linz. The recent trends in music recommendation research have been reviewed in:

  1. Current Challenges and Visions in Music Recommender Systems Research Markus Schedl, Hamed Zamani, Ching-Wei Chen, Yashar Deldjoo, and Mehdi Elahi International Journal of Multimedia Information Retrieval, 2018 (IJMIR) [Preprint]

For this challenge, Spotify has released a dataset containing one million playlists generated by Spotify users. To access the dataset, please visit https://recsys-challenge.spotify.com/.

Tweet Rating Dataset

This dataset contains tweets of users about the items of four popular and diverse web applications: IMDb (movie), YouTube (video clip), Pandora (music), and Goodreads (book). This dataset contains ~500K tweets from ~20K users about ~230K items (movie, music, etc.). This dataset is freely available for research purposes. Tweet Rating Dataset can be downloaded from here. Citation:

  1. Adaptive User Engagement Evaluation via Multi-task Learning Hamed Zamani, Pooya Moradi, and Azadeh Shakery In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015 (SIGIR ’15)

PAL: Preference/Ranking Aggregation Library

This Ruby library contains a few simple rank aggregation methods that we used in ACM RecSysChallenge 2014. The package is open-sourced and can be found here. Citation:

  1. Regression and Learning to Rank Aggregation for User Engagement Evaluation Hamed Zamani, Azadeh Shakery, and Pooya Moradi In Proceedings of the 2014 Recommender Systems Challenge, 2014 (RecSysChallenge ’14)

Wikipedia English-Persian Parallel Corpus

This parallel corpus is automatically extracted from English and Persian Wikipedia articles. We extensively evaluate our created parallel corpus to show its high quality compared to the existing English-Persian parallel corpora. This dataset is freely available for research purposes. To download the parallel corpus, please visit here. Citation:

  1. Sentence Alignment Using Local and Global Information Hamed Zamani, Heshaam Faili, and Azadeh Shakery Computer Speech & Language, 2016 (CSL)