- Introducing Mahout
- Recommendations
- First Recommender Engine
- Preference data
- PreferenceArray
- FastMap: Implementation of Map
- FastByIDMap
- FastIDSet
- DataModel
- GenericDataModel
- FileDataModel
- MySQLJDBCDataModel
- Generating Recommendations for Boolean data sets
- Item Similarity Vs User similarity
- User similarity
- CityBlockSimilarity : Compute User similarity
- EuclideanDistanceSimilarity
- PearsonCorrelationSimilarity : Compute User similarity
- SpearmanCorrelationSimilarity : Compute User similarity
- LogLikelihoodSimilarity : Compute User similarity
- CachingUserSimilarity: Compute User similarity
- UncenteredCosineSimilarity: Compute User similarity
- TanimotoCoefficientSimilarity: Compute User similarity
- Item similarity
- CityBlockSimilarity: Compute item similarity
- EuclideanDistanceSimilarity : Compute item similarity
- PearsonCorrelationSimilarity : Compute item similarity
- LogLikelihoodSimilarity : Compute item similarity
- UncenteredCosineSimilarity : Compute item similarity
- TanimotoCoefficientSimilarity : Compute item similarity
- CachingItemSimilarity : Compute item similarity
- UserNeighborhood
- NearestNUserNeighborhood
- ThresholdUserNeighborhood
- Recommender interface
- GenericUserBasedRecommender
- GenericBooleanPrefUserBasedRecommender
- SlopeOneRecommender
- CachingRecommender
- GenericItemBasedRecommender
- GenericBooleanPrefItemBasedRecommender
- ItemAverageRecommender
- ItemUserAverageRecommender
- SVDRecommender
- KnnItemBasedRecommender
- Cluster based recommendations
Sunday 16 August 2015
mahout tutorial
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