This example shows how to limit the vertices and edges used. Teradata recommends this technique if the original Vertices and Edges tables are large, but only a subset of the information is relevant. The function calculates the hubs and authorities for the nodes specified in the Sources and Targets tables (user_source_nodes and product_target_nodes).
Input
- Vertices: user_product_nodes, which has customer names
- Edges: women_apparel_log, which reflects customer shopping patterns
SQL Call
SELECT * FROM PSALSA (
ON user_product_nodes AS Vertices PARTITION BY nodename
ON women_apparel_log AS Edges PARTITION BY username
ON user_source_nodes AS Sources PARTITION BY username
ON product_target_nodes AS Targets PARTITION BY product
USING
SourceKey ('username')
TargetKey ('product')
EdgeWeight ('frequency')
MaxHubNum (2)
MaxAuthorityNum (2)
TeleportProb (0.15)
RandomWalkLength (500)
) AS dt ORDER BY username, hub_score DESC, authority_score DESC;
Output
Based on the output, the retailer recommends pajamas to Sandra, who has no purchase history for them, but recommends nothing to Susan, who has bought all the items (see the PSALSA Example: Product Recommendation for Similar Users input). The *_score results vary with every run.
username hub_username hub_score authority_product authority_score -------- ------------ --------- ----------------- ------------------- sandra sally 0.364 NULL NULL sandra stephanie 0.088 NULL NULL sandra NULL NULL pajamas 0.21348314606741572 susan sally 0.228 NULL NULL susan sandra 0.168 NULL NULL
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