Pith. sign in

REVIEW 2 cited by

KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1807.11141 v7 pith:WBO43KU5 submitted 2018-07-30 cs.IR

classification cs.IR
keywords linkedrecommenderdatasetinformationknowledgesystemsdatasetsfirst
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

To develop a knowledge-aware recommender system, a key data problem is how we can obtain rich and structured knowledge information for recommender system (RS) items. Existing datasets or methods either use side information from original recommender systems (containing very few kinds of useful information) or utilize private knowledge base (KB). In this paper, we present the first public linked KB dataset for recommender systems, named KB4Rec v1.0, which has linked three widely used RS datasets with the popular KB Freebase. Based on our linked dataset, we first preform some interesting qualitative analysis experiments, in which we discuss the effect of two important factors (i.e. popularity and recency) on whether a RS item can be linked to a KB entity. Finally, we present the comparison of several knowledge-aware recommendation algorithms on our linked dataset.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation

    cs.IR 2019-08 conditional novelty 7.0 of 10

    The paper introduces NI and KNI, graph-based recommendation models that predict directly from neighbor-pair interactions, and reports large AUC and top-N gains over five baseline families.

  2. GraphSW: a training protocol based on stage-wise training for GNN-based Recommender Model

    cs.IR 2019-08 conditional novelty 5.0 of 10

    Stage-wise training with neighbor resampling improves KGCN and RippleNet on most tested datasets and helps KGCN converge at higher graph hops, though a few Recall@K entries drop.

Pith tools