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Tag based Product Recommendation System Using Rating Variance

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-0-387-85820-3openalex-extracted

Recommender Systems Handbook

inferredcites →https://doi.org/10.1016/j.elerap.2009.08.004openalex-extracted

Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation

inferredcites →https://doi.org/10.1109/ic3i.2016.7917941openalex-extracted

Efficient tag based personalised collaborative movie reccommendation system

inferredcites →https://doi.org/10.1109/wkdd.2010.54openalex-extracted

User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop

inferredcites →https://doi.org/10.1145/138859.138867openalex-extracted

Using collaborative filtering to weave an information tapestry

inferredcites →https://doi.org/10.1145/1454008.1454059openalex-extracted

Improving top-n recommendation techniques using rating variance

inferredcites →https://doi.org/10.1145/1506250.1506255openalex-extracted

A hybrid approach to item recommendation in folksonomies

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1108/k-03-2019-0199openalex-snapshot

A new model for assessing the role of customer behavior history, product classification, and prices on the success of the recommender systems in e-commerce

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