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On Scalability of Association-rule-based Recommendation

https://doi.org/10.1145/3398202
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What this work says

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

inferredcites →https://doi.org/10.1007/978-0-230-37405-8_1openalex-snapshot
Introduction to algorithms
inferredcites →https://doi.org/10.1007/978-3-319-29659-3openalex-snapshot
Recommender Systems: The Textbook
inferredcites →https://doi.org/10.1007/pl00011656openalex-snapshot
Parallel Data Mining for Association Rules on Shared-Memory Systems
inferredcites →https://doi.org/10.1007/s10115-006-0002-1openalex-snapshot
A collaborative filtering framework based on fuzzy association rules and multiple-level similarity
inferredcites →https://doi.org/10.1007/s10618-006-0043-9openalex-snapshot
Hyperclique pattern discovery
inferredcites →https://doi.org/10.1007/s11042-017-4406-6openalex-snapshot
A recommendation engine for travel products based on topic sequential patterns
inferredcites →https://doi.org/10.1016/j.eswa.2004.05.005openalex-snapshot
Effective personalized recommendation based on time-framed navigation clustering and association mining
inferredcites →https://doi.org/10.1016/j.eswa.2009.06.010openalex-snapshot
Effective page recommendation algorithms based on distributed learning automata and weighted association rules
inferredcites →https://doi.org/10.1023/a:1009839827683openalex-snapshot
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
inferredcites →https://doi.org/10.1023/a:1013232803866openalex-snapshot
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
inferredcites →https://doi.org/10.1023/a:1013284820704openalex-snapshot
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
inferredcites →https://doi.org/10.1109/access.2016.2549182openalex-snapshot
Fast Recommendations With the M-Distance
inferredcites →https://doi.org/10.1109/cie.2002.1185862openalex-snapshot
Building a recommender agent for e-learning systems
inferredcites →https://doi.org/10.1109/icdm.2001.989492openalex-snapshot
Proceedings 2001 IEEE International Conference on Data Mining
inferredcites →https://doi.org/10.1109/icdm.2004.10116openalex-snapshot
Mining Frequent Itemsets from Secondary Memory
inferredcites →https://doi.org/10.1109/icdm.2013.158openalex-snapshot
MLI: An API for Distributed Machine Learning
inferredcites →https://doi.org/10.1109/icdmw.2014.157openalex-snapshot
Popular Items or Niche Items: Flexible Recommendation Using Cosine Patterns
inferredcites →https://doi.org/10.1109/mic.2003.1167344openalex-snapshot
Amazon.com recommendations: item-to-item collaborative filtering
inferredcites →https://doi.org/10.1109/tii.2014.2308433openalex-snapshot
An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems
inferredcites →https://doi.org/10.1109/tkde.2005.166openalex-snapshot
Fast algorithms for frequent itemset mining using FP-trees
inferredcites →https://doi.org/10.1109/tkde.2005.99openalex-snapshot
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
inferredcites →https://doi.org/10.1109/tkde.2008.229openalex-snapshot
Predicting Missing Items in Shopping Carts
inferredcites →https://doi.org/10.1109/tkde.2016.2541149openalex-snapshot
Mining User-Aware Rare Sequential Topic Patterns in Document Streams
inferredcites →https://doi.org/10.1109/tsc.2013.40openalex-snapshot
Workload Analysis, Implications, and Optimization on a Production Hadoop Cluster: A Case Study on Taobao
inferredcites →https://doi.org/10.1109/wi.2003openalex-snapshot
Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)
inferredcites →https://doi.org/10.1137/0117039openalex-snapshot
Bounds on Multiprocessing Timing Anomalies
inferredcites →https://doi.org/10.1145/1150402.1150416openalex-snapshot
Out-of-core frequent pattern mining on a commodity PC
inferredcites →https://doi.org/10.1145/1297231.1297249openalex-snapshot
Robustness of collaborative recommendation based on association rule mining
inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.1145/1835449.1835467openalex-snapshot
Query forwarding in geographically distributed search engines
inferredcites →https://doi.org/10.1145/1864708.1864770openalex-snapshot
The YouTube video recommendation system
inferredcites →https://doi.org/10.1145/2365952.2365979openalex-snapshot
Context-aware music recommendation based on latenttopic sequential patterns
inferredcites →https://doi.org/10.1145/2700496openalex-snapshot
Relevance Meets Coverage
inferredcites →https://doi.org/10.1145/2856037openalex-snapshot
A Novel Evidence-Based Bayesian Similarity Measure for Recommender Systems
inferredcites →https://doi.org/10.1145/2907948openalex-snapshot
Scalable and Efficient Web Search Result Diversification
inferredcites →https://doi.org/10.1145/2926718openalex-snapshot
Sound and Music Recommendation with Knowledge Graphs
inferredcites →https://doi.org/10.1145/3054952openalex-snapshot
Multirelational Recommendation in Heterogeneous Networks
inferredcites →https://doi.org/10.1145/3079628.3079699openalex-snapshot
A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems
inferredcites →https://doi.org/10.1145/3106426.3106542openalex-snapshot
Bitwise parallel association rule mining for web page recommendation
inferredcites →https://doi.org/10.1145/3159652.3159656openalex-snapshot
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
inferredcites →https://doi.org/10.1145/342009.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1145/347090.347112openalex-snapshot
Efficient search for association rules
inferredcites →https://doi.org/10.1145/502932.502935openalex-snapshot
Effective personalization based on association rule discovery from web usage data
inferredcites →https://doi.org/10.1287/ijoc.1050.0153openalex-snapshot
Validation Sequence Optimization: A Theoretical Approach
inferredcites →https://doi.org/10.1287/ijoc.2013.0575openalex-snapshot
Association Rules for Recommendations with Multiple Items
inferredcites →https://doi.org/10.1287/isre.2015.0583openalex-snapshot
Recommendations Using Information from Multiple Association Rules: A Probabilistic Approach

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