Basic and Utility Algorithms¶
The lenskit.algorithms.basic module contains baseline and utility algorithms
for nonpersonalized recommendation and testing.
Personalized Mean Rating Prediction¶
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class
lenskit.algorithms.basic.Bias(items=True, users=True, damping=0.0)¶ Bases:
lenskit.algorithms.PredictorA user-item bias rating prediction algorithm. This implements the following predictor algorithm:
\[s(u,i) = \mu + b_i + b_u\]where \(\mu\) is the global mean rating, \(b_i\) is item bias, and \(b_u\) is the user bias. With the provided damping values \(\beta_{\mathrm{u}}\) and \(\beta_{\mathrm{i}}\), they are computed as follows:
\[\begin{align*} \mu & = \frac{\sum_{r_{ui} \in R} r_{ui}}{|R|} & b_i & = \frac{\sum_{r_{ui} \in R_i} (r_{ui} - \mu)}{|R_i| + \beta_{\mathrm{i}}} & b_u & = \frac{\sum_{r_{ui} \in R_u} (r_{ui} - \mu - b_i)}{|R_u| + \beta_{\mathrm{u}}} \end{align*}\]The damping values can be interpreted as the number of default (mean) ratings to assume a priori for each user or item, damping low-information users and items towards a mean instead of permitting them to take on extreme values based on few ratings.
Parameters: - items – whether to compute item biases
- users – whether to compute user biases
- damping (number or tuple) – Bayesian damping to apply to computed biases. Either a number, to damp both user and item biases the same amount, or a (user,item) tuple providing separate damping values.
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mean_¶ The global mean rating.
Type: double
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item_offsets_¶ The item offsets (\(b_i\) values)
Type: pandas.Series
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user_offsets_¶ The item offsets (\(b_u\) values)
Type: pandas.Series
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fit(data)¶ Train the bias model on some rating data.
Parameters: data (DataFrame) – a data frame of ratings. Must have at least user, item, and rating columns. Returns: the fit bias object. Return type: Bias
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predict_for_user(user, items, ratings=None)¶ Compute predictions for a user and items. Unknown users and items are assumed to have zero bias.
Parameters: - user – the user ID
- items (array-like) – the items to predict
- ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, will be used to recompute the user’s bias at prediction time.
Returns: scores for the items, indexed by item id.
Return type:
Fallback Predictor¶
The Fallback rating predictor is a simple hybrid that takes a list of composite algorithms,
and uses the first one to return a result to predict the rating for each item.
A common case is to fill in with Bias when a primary predictor cannot score an item.
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class
lenskit.algorithms.basic.Fallback(algorithms, *others)¶ Bases:
lenskit.algorithms.PredictorThe Fallback algorithm predicts with its first component, uses the second to fill in missing values, and so forth.
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fit(ratings, *args, **kwargs)¶ Train a model using the specified ratings (or similar) data.
Parameters: - ratings (pandas.DataFrame) – The ratings data.
- args – Additional training data the algorithm may require.
- kwargs – Additional training data the algorithm may require.
Returns: The algorithm object.
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predict_for_user(user, items, ratings=None)¶ Compute predictions for a user and items.
Parameters: - user – the user ID
- items (array-like) – the items to predict
- ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns: scores for the items, indexed by item id.
Return type:
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Memorized Predictor¶
The Memorized recommender is primarily useful for test cases. It memorizes a set of
rating predictions and returns them.
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class
lenskit.algorithms.basic.Memorized(scores)¶ Bases:
lenskit.algorithms.PredictorThe memorized algorithm memorizes socres provided at construction time.
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fit(*args, **kwargs)¶ Train a model using the specified ratings (or similar) data.
Parameters: - ratings (pandas.DataFrame) – The ratings data.
- args – Additional training data the algorithm may require.
- kwargs – Additional training data the algorithm may require.
Returns: The algorithm object.
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predict_for_user(user, items, ratings=None)¶ Compute predictions for a user and items.
Parameters: - user – the user ID
- items (array-like) – the items to predict
- ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns: scores for the items, indexed by item id.
Return type:
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