Tuning Personalized PageRank for Semantics-aware Recommendations based on Linked Open Data

Published on 2017-07-101454 Views

Cataldo Musto

In this article we investigate how the knowledge available in the Linked Open Data cloud (LOD) can be exploited to improve the effectiveness of a semantics-aware graph-based recommendation framework b

ESWC 2017 - Portorož

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Presentation

Tuning Personalized PageRank for Semantics-aware Recommendations based on Linked Open Data00:00

Recommender Systems00:19

Recommender Systems - 100:44

Recommendation Paradigms01:13

Recommendation Paradigms - 101:24

Recommendation Paradigms - 201:46

Recommendation Paradigms - 302:20

Graph-based RecSys - 404:18

Wikipedia04:46

DBpedia05:11

Linked Open Data (cloud)05:47

Linked Open Data (cloud) - 105:58

Introducing Linked Open Data06:04

Introducing Linked Open Data - 106:34

Graph-based RecSys06:56

Graph-based RecSys - 107:09

Graph-based RecSys - 207:24

Graph-based RecSys - 307:30

How to get the recommendations?07:49

Graph-based RecSys - 508:19

Graph-based RecSys - 608:23

Graph-based RecSys - 708:32

Graph-based RecSys - 808:55

Graph-based RecSys - 909:11

Graph-based RecSys - 1009:23

Graph-based RecSys - 1109:35

Graph-based RecSys - 1209:38

Graph-based RecSys - 1309:43

Graph-based RecSys - 1409:47

Graph-based RecSys - 1509:54

Graph-based RecSys - 1610:02

Graph-based RecSys - 1710:10

Graph-based RecSys - 1810:32

Graph-based RecSys - 1910:40

Graph-based RecSys - 2010:49

Graph-based RecSys - 2111:01

Graph-based RecSys - 2211:13

Graph-based RecSys - 2311:25

Experiments11:36

Research Questions11:41

Research Questions - 112:00

Experimental Evaluation12:12

Experimental Evaluation - 112:35

Experimental Evaluation - 212:55

Experimental Evaluation - 313:09

Experimental Evaluation - 413:10

Experimental Evaluation - 513:22

Experimental Evaluation - 613:30

Experimental Evaluation - 713:38

Experimental Evaluation - 813:55

Experimental Evaluation - 914:16

Experimental Evaluation - 1014:32

Experimental Evaluation - 1114:39

Experiment 114:51

Experiment 1 - 114:56

Experiment 1 - 215:05

Experiment 1 - 315:26

Experiment 1 - 415:28

Experiment 1 - 515:51

Experiment 216:09

Experiment 2 - 116:15

Experiment 2 - 216:26

Experiment 2 - 317:00

Experiment 2 - 417:02

Experiment 2 - 517:07

Take-Home Message17:31

Experiment 317:43

Experiment 3 - 118:08

Experiment 3 - 218:19

Experiment 3 - 318:26

Conclusions18:33

Recap18:36

Lessons learned18:59

Questions19:22