Learning to Recommend Items to Wikidata Editors

Wikidata is an open knowledge graph built by a global community of
volunteers. As it advances in scale, it faces substantial challenges around editor
engagement. These challenges are in terms of both attracting new editors to keep
up with the sheer amount of work, and retaining existing editors. Experience from
other online communities and peer-production systems, including Wikipedia, suggests that personalised recommendations could help, especially newcomers, who are sometimes unsure about how to contribute best to an ongoing effort. For
this reason, we propose a recommender system WikidataRec for Wikidata items.
The system uses a hybrid of content-based and collaborative filtering techniques
to rank items for editors relying on both item features and item-editor previous
interaction. A neural network, named neural mixture of representations, is designed to learn fine weights for the combination of item-based representations
and optimize them with editor-based representation by item-editor interaction. To facilitate further research in this space, we also create two benchmark datasets, a general-purpose one with 220,000 editors responsible for 14 million interactions with 4 million items, and a second one focusing on the contributions of more than 8,000 more active editors. We perform an offline evaluation of the system on both datasets with promising results. Our code and datasets are available at https://github.com/WikidataRec-developer/Wikidata_Recommender.