Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity
The Heterogeneous Information Network (HIN) formalism is very flexible and enables complex recommendations models. We evaluate the effect of different parts of a HIN on the accuracy and the diversity of recommendations, then investigate if these effects are only due to the semantic content encoded i...
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| creator | Morales, Pedro Ramaciotti Tabourier, Lionel Fournier-S'niehotta, Raphaël |
| description | The Heterogeneous Information Network (HIN) formalism is very flexible and
enables complex recommendations models. We evaluate the effect of different
parts of a HIN on the accuracy and the diversity of recommendations, then
investigate if these effects are only due to the semantic content encoded in
the network. We use recently-proposed diversity measures which are based on the
network structure and better suited to the HIN formalism. Finally, we randomly
shuffle the edges of some parts of the HIN, to empty the network from its
semantic content, while leaving its structure relatively unaffected. We show
that the semantic content encoded in the network data has a limited importance
for the performance of a recommender system and that structure is crucial. |
| doi_str_mv | 10.48550/arxiv.2011.03796 |
| format | Article |
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enables complex recommendations models. We evaluate the effect of different
parts of a HIN on the accuracy and the diversity of recommendations, then
investigate if these effects are only due to the semantic content encoded in
the network. We use recently-proposed diversity measures which are based on the
network structure and better suited to the HIN formalism. Finally, we randomly
shuffle the edges of some parts of the HIN, to empty the network from its
semantic content, while leaving its structure relatively unaffected. We show
that the semantic content encoded in the network data has a limited importance
for the performance of a recommender system and that structure is crucial.</description><identifier>DOI: 10.48550/arxiv.2011.03796</identifier><language>eng</language><subject>Computer Science - Information Retrieval ; Computer Science - Social and Information Networks</subject><creationdate>2020-11</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2011.03796$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2011.03796$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Morales, Pedro Ramaciotti</creatorcontrib><creatorcontrib>Tabourier, Lionel</creatorcontrib><creatorcontrib>Fournier-S'niehotta, Raphaël</creatorcontrib><title>Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity</title><description>The Heterogeneous Information Network (HIN) formalism is very flexible and
enables complex recommendations models. We evaluate the effect of different
parts of a HIN on the accuracy and the diversity of recommendations, then
investigate if these effects are only due to the semantic content encoded in
the network. We use recently-proposed diversity measures which are based on the
network structure and better suited to the HIN formalism. Finally, we randomly
shuffle the edges of some parts of the HIN, to empty the network from its
semantic content, while leaving its structure relatively unaffected. We show
that the semantic content encoded in the network data has a limited importance
for the performance of a recommender system and that structure is crucial.</description><subject>Computer Science - Information Retrieval</subject><subject>Computer Science - Social and Information Networks</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz8tKw0AYBeDZuJDqA7hyXiBxLp1LlqXeCgXBFLfhz8yfdsBJymRSzNur0dXhwOHAR8gdZ-XaKsUeIH2FSykY5yWTptLX5OOAYw79keYT0l08g8t06GiNEfoc3Eih97TOaXJ5SkiHnr6jG2LE3kMOP3Xj3JTAzcvwMVwwjSHPN-Sqg88Rb_9zRernp8P2tdi_vey2m30B2uhCdtYj7zTY1nh0ohWoOqWMbCW3HLwyCLA2kguvGFjBqlZry53Xlaoklyty__e6uJpzChHS3Pz6msUnvwEsr0uJ</recordid><startdate>20201107</startdate><enddate>20201107</enddate><creator>Morales, Pedro Ramaciotti</creator><creator>Tabourier, Lionel</creator><creator>Fournier-S'niehotta, Raphaël</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20201107</creationdate><title>Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity</title><author>Morales, Pedro Ramaciotti ; Tabourier, Lionel ; Fournier-S'niehotta, Raphaël</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-3f8de1f6a8b7dec2b2e5f5573b3181ad57eaa47312d50a8209b6681cd6959313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Information Retrieval</topic><topic>Computer Science - Social and Information Networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Morales, Pedro Ramaciotti</creatorcontrib><creatorcontrib>Tabourier, Lionel</creatorcontrib><creatorcontrib>Fournier-S'niehotta, Raphaël</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Morales, Pedro Ramaciotti</au><au>Tabourier, Lionel</au><au>Fournier-S'niehotta, Raphaël</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity</atitle><date>2020-11-07</date><risdate>2020</risdate><abstract>The Heterogeneous Information Network (HIN) formalism is very flexible and
enables complex recommendations models. We evaluate the effect of different
parts of a HIN on the accuracy and the diversity of recommendations, then
investigate if these effects are only due to the semantic content encoded in
the network. We use recently-proposed diversity measures which are based on the
network structure and better suited to the HIN formalism. Finally, we randomly
shuffle the edges of some parts of the HIN, to empty the network from its
semantic content, while leaving its structure relatively unaffected. We show
that the semantic content encoded in the network data has a limited importance
for the performance of a recommender system and that structure is crucial.</abstract><doi>10.48550/arxiv.2011.03796</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Information Retrieval Computer Science - Social and Information Networks |
| title | Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity |
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