Basic and Utility Algorithms¶
The lenskit.algorithms.basic
module contains baseline and utility algorithms
for nonpersonalized recommendation and testing.
Personalized Mean Rating Prediction¶

class
lenskit.algorithms.basic.
Bias
(items=True, users=True, damping=0.0)¶ Bases:
lenskit.algorithms.Predictor
A useritem 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 lowinformation 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.

mean_
¶ The global mean rating.
Type: double

item_offsets_
¶ The item offsets (\(b_i\) values)
Type: pandas.Series

user_offsets_
¶ The item offsets (\(b_u\) values)
Type: pandas.Series

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

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 (arraylike) – 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.

class
lenskit.algorithms.basic.
Fallback
(algorithms, *others)¶ Bases:
lenskit.algorithms.Predictor
The Fallback algorithm predicts with its first component, uses the second to fill in missing values, and so forth.

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.

predict_for_user
(user, items, ratings=None)¶ Compute predictions for a user and items.
Parameters:  user – the user ID
 items (arraylike) – 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:

Memorized Predictor¶
The Memorized
recommender is primarily useful for test cases. It memorizes a set of
rating predictions and returns them.

class
lenskit.algorithms.basic.
Memorized
(scores)¶ Bases:
lenskit.algorithms.Predictor
The memorized algorithm memorizes socres provided at construction time.

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.

predict_for_user
(user, items, ratings=None)¶ Compute predictions for a user and items.
Parameters:  user – the user ID
 items (arraylike) – 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:
