A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing

TL;DR

This paper introduces a large-scale, rich-context dataset from an online knowledge-sharing platform, enabling diverse recommendation and user behavior studies, with potential applications in multiple research tasks.

Contribution

It provides the largest real-world interaction dataset for personalized recommendation, including user queries and various annotations, facilitating comprehensive research in recommendation systems.

Findings

Dataset contains 100 million interactions over 10 days.

Demonstrates dataset's utility in multiple recommendation tasks.

Supports research beyond recommendation, like user gender prediction.

Abstract

Data plays a vital role in machine learning studies. In the research of recommendation, both user behaviors and side information are helpful to model users. So, large-scale real scenario datasets with abundant user behaviors will contribute a lot. However, it is not easy to get such datasets as most of them are only hold and protected by companies. In this paper, a new large-scale dataset collected from a knowledge-sharing platform is presented, which is composed of around 100M interactions collected within 10 days, 798K users, 165K questions, 554K answers, 240K authors, 70K topics, and more than 501K user query keywords. There are also descriptions of users, answers, questions, authors, and topics, which are anonymous. Note that each user's latest query keywords have not been included in previous open datasets, which reveal users' explicit information needs. We characterize the dataset and demonstrate its potential applications for recommendation study. Multiple experiments show the dataset can be used to evaluate algorithms in general top-N recommendation, sequential recommendation, and context-aware recommendation. This dataset can also be used to integrate search and recommendation and recommendation with negative feedback. Besides, tasks beyond recommendation, such as user gender prediction, most valuable answerer identification, and high-quality answer recognition, can also use this dataset. To the best of our knowledge, this is the largest real-world interaction dataset for personalized recommendation.