61 datasets found

Formats: JSON Tags: Recommendation Systems

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  • Taobao User Behavior

    The Taobao User Behavior dataset is a subset of user behaviors on Taobao collected within 9 days and consists of more than 70 million samples and 1 million users.

  • FourSquare shopping places dataset

    FourSquare shopping places dataset from 5 cities in Indonesia. It consists of 1) 176 shopping places data, 2) 3844 visitors' reviews, and 3) 14309 users data.

  • MovieLens100K

    The dataset is used for sequential recommendation tasks, and it contains user-item interaction history.

  • Netflix Dataset

    The dataset used in the paper is a Netflix dataset, which is a large-scale matrix factorization problem.

  • Bing-News

    Bing-News is a dataset containing 1,025,192 pieces of implicit feedback collected from the server logs of Bing News.

  • Extreme Classification Repository

    The Extreme Classification Repository (ECR) contains datasets for extreme multi-label classification.

  • Taobao Dataset

    Precise user modeling is critical for online personalized recommen-der services. Generally, users’ interests are diverse and are not limited to a single aspect, which is...

  • Amazon Book Dataset

    Amazon book data set contains book reviews and metadata from Amazon. Following previous work, we regard reviews as positive samples and randomly select products not rated by a...

  • Alibaba Display Advertising Platform Dataset

    The production data set collected from Alibaba display advertising platform. We randomly select about 1% of 1-day samples for training and 0.1% of the next-day samples for...

  • Jester Dataset

    The Jester dataset contains joke ratings in a continuous scale from 1 to 10 for 100 jokes from a total of 73421 users.

  • Foursquare

    Location prediction forecasts a user’s location based on historical user mobility traces. To tackle the intrinsic sparsity issue of real-world user mobility traces,...

  • Amazon dataset

    The Amazon dataset is used to evaluate the performance of the proposed approach. It consists of 2000 users, 1500 items, 86690 reviews, 7219 number ratings, 3.6113 average number...

  • Steam

    The dataset used in the paper is a large online video game distribution platform. The dataset contains 2,567,538 users, 15,474 games and 7,793,069 English reviews spanning...

  • MovieLens-25M

    The dataset used in the Controllable Gradient Item Retrieval paper, which consists of user-item interaction data and item-attribute relation data.

  • Netflix

    A dataset for interactive recommender systems, used to evaluate the proposed Tree-structured Policy Gradient Recommendation (TPGR) framework.

  • Ciao

    Ciao: an online consumer shopping site that records users' ratings of items with timestamps.

  • KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed...

    KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

  • Epinions

    The Epinions dataset is a large-scale opinion mining dataset. It contains 1 million user-item interactions and is widely used for evaluating the performance of recommender systems.

  • ML-1M

    The dataset used in this paper is a large-scale dataset for implicit feedback, consisting of user-item interactions, where each interaction is a pair of a user and an item.

  • MovieLens-1M, Foursquare, and Yelp2018

    The dataset used in the paper is MovieLens-1M, Foursquare, and Yelp2018. These datasets are used for top-k recommendation task.
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