org.grouplens.lenskit.basic

Class AbstractItemRecommender

java.lang.Object

org.grouplens.lenskit.basic.AbstractItemRecommender

All Implemented Interfaces:

ItemRecommender

Direct Known Subclasses:

TopNItemRecommender

public abstract class AbstractItemRecommender
extends Object
implements ItemRecommender

Base class for item recommenders. It implements all methods required by ItemRecommender by delegating them to general methods with fastutil-based interfaces.

Constructor Summary

Constructors

Constructor and Description
AbstractItemRecommender()

Method Summary

Modifier and Type	Method and Description
List<ScoredId>	recommend(long user) Recommend all possible items for a user using the default exclude set.
List<ScoredId>	recommend(long user, int n) Recommend up to n items for a user using the default exclude set.
protected abstract List<ScoredId>	recommend(long user, int n, LongSet candidates, LongSet exclude) Implementation method for ID-based recommendation.
List<ScoredId>	recommend(long user, int n, Set<Long> candidates, Set<Long> exclude) Produce a set of recommendations for the user.
List<ScoredId>	recommend(long user, Set<Long> candidates) Recommend all possible items for a user from a set of candidates using the default exclude set.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

AbstractItemRecommender

public AbstractItemRecommender()

Method Detail

recommend

public List<ScoredId> recommend(long user)

Recommend all possible items for a user using the default exclude set.

Delegates to recommend(long, int, LongSet, LongSet).

Specified by:

recommend in interface ItemRecommender

Parameters:

user - The user ID.

Returns:

The recommended items.

See Also:

ItemRecommender.recommend(long, int, Set, Set)

recommend

public List<ScoredId> recommend(long user,
                                int n)

Recommend up to n items for a user using the default exclude set.

Delegates to recommend(long, int, LongSet, LongSet).

Specified by:

recommend in interface ItemRecommender

Parameters:

user - The user ID.

n - The number of recommendations to return.

Returns:

The recommended items.

See Also:

ItemRecommender.recommend(long, int, Set, Set)

recommend

public List<ScoredId> recommend(long user,
                                Set<Long> candidates)

Recommend all possible items for a user from a set of candidates using the default exclude set.

Delegates to recommend(long, int, LongSet, LongSet).

Specified by:

recommend in interface ItemRecommender

Parameters:

user - The user ID.

candidates - The candidate set (can be null to represent the universe).

Returns:

The recommended items.

See Also:

ItemRecommender.recommend(long, int, Set, Set)

recommend

public List<ScoredId> recommend(long user,
                                int n,
                                @Nullable
                                Set<Long> candidates,
                                @Nullable
                                Set<Long> exclude)

Produce a set of recommendations for the user. This is the most general recommendation method, allowing the recommendations to be constrained by both a candidate set and an exclude set. The exclude set is applied to the candidate set, so the final effective candidate set is canditates minus exclude.

Delegates to recommend(long, int, LongSet, LongSet).

Specified by:

recommend in interface ItemRecommender

Parameters:

user - The user's ID

n - The number of ratings to return. If negative, there is no specific recommendation list size requested.

candidates - A set of candidate items which can be recommended. If null, all items are considered candidates.

exclude - A set of items to be excluded. If null, a default exclude set is used.

Returns:

A list of recommended items. If the recommender cannot assign meaningful scores, the scores will be Double.NaN. For most scoring recommenders, the items will be ordered in decreasing order of score. This is not a hard requirement — e.g. set recommenders are allowed to be more flexible.

recommend

protected abstract List<ScoredId> recommend(long user,
                                            int n,
                                            @Nullable
                                            LongSet candidates,
                                            @Nullable
                                            LongSet exclude)

Implementation method for ID-based recommendation. All other ID-based methods are implemented in terms of this one.

Parameters:

user - The user ID.

n - The number of items to return, or negative to return all possible items.

candidates - The candidate set, or null to use a default set of candidates.

exclude - The set of excluded items, or null to use the default exclude set.

Returns:

A scored list of item IDs.

See Also:

ItemRecommender.recommend(long, int, Set, Set)