Model & Train#

Pure and Feat model#

LibRecommender is a hybrid recommender system, which means you can choose whether to use features other than user behaviors or not. For models only use user behaviors, we classify them as pure models. This category includes UserCF, ItemCF, SVD, SVD++, ALS, NCF, BPR, RNN4Rec, Item2Vec, Caser, WaveNet, DeepWalk, NGCF, LightGCN.

Then for models that can include other features (e.g., age, sex, name etc.), we call them feat models. This category includes WideDeep, FM, DeepFM, YouTubeRetrieval, YouTubeRanking, AutoInt, DIN, GraphSage, PinSage.

The main difference on usage between these two kinds of models are:

1. pure models should use DatasetPure to process data, and feat models should use DatasetFeat to process data.

2. When using feat models, four parameters should be provided, i.e. [sparse_col, dense_col, user_col, item_col], as otherwise the model will have no idea how to deal with all kinds of features.

The fit() method is the sole method for training a model in LibRecommender. You can find some typical usages in these examples:

See also

In fact, there exists two other kinds of model categories in LibRecommender, and we call them sequence and graph models. You can find them in the algorithm list.

Sequence models leverage information of user behavior sequence, whereas Graph models leverage information from graph. As you can see, these models overlap with pure and feat models. But no need to worry, the APIs remain the same, and you can just refer to the examples above.

Loss#

LibRecommender provides some options on loss type for ranking task. The default loss type for ranking is cross entropy loss. Since version 0.10.0, focal loss was added into the library. First introduced in Lin et al., 2018, focal loss down-weights well-classified examples and focuses on hard examples to get better training performance, and here is the implementation.

In order to choose which loss to use, simply set the loss_type parameter:

>>> model = Caser(task="ranking", loss_type="cross_entropy", ...)
>>> model = Caser(task="ranking", loss_type="focal", ...)

There are some special cases:

  • Some algorithms are hard to assign explicit loss type, including UserCF, ItemCF, ALS, Item2Vec, DeepWalk, so they don’t have loss_type parameter.

  • As its name suggests, BPR can only use bpr loss.

  • The YouTubeRetrieval algorithm is also different, its loss_type is either sampled_softmax or nce.

  • Finally, with RNN4Rec algorithm, one can choose three loss_type, i.e. cross_entropy, focal, bpr.

We are aware that these loss restrictions are hard to remember at once, so this leaves room for further improvements:)