Recommendation#

By default, the recommendation result returned by model.recommend_user() method will filter out items that a user has previously consumed.

However, if you use a very large n_rec and number of consumed items for this user plus n_rec exceeds number of total items, i.e. len(user_consumed) + n_rec > n_items, the consumed items will not be filtered out since there are not enough items to recommend. If you don’t want to filter out consumed items, set filter_consumed=False.

LibRecommender also supports random recommendation by setting random_rec=True (By default it is False). Of course, it’s not completely random, but random sampling based on each item’s prediction scores. It’s basically a trade-off between accuracy and diversity.

Finally, batch recommendation is also supported by simply passing a list to the user parameter. The returned result will be a dict, with users as keys and numpy.array as values.

>>> model.recommend_user(user=[1, 2, 3], n_rec=3, filter_consumed=True, random_rec=False)
# returns {1: array([2529, 1196, 2916]), 2: array([ 541,  750, 1299]), 3: array([3183, 2722, 2672])}

Cold Start#

It is very common to encounter new users or items that doesn’t exist in training data, which is hard to make recommendations for them. This is the notorious “cold-start” problem in recommender system.

There are two strategies in LibRecommender to handle the cold-start problem: popular and average. The popular strategy simply returns the most popular items in training data.

The average strategy means using the average of all the user/item embeddings as the representation of the cold-start user/item. Once we have the embedding, we can make predictions and recommendations. This strategy indicates that a cold-start user/item’s behavior is treated as the “average” behavior of all the known users/items.

Likewise, the new category of one feature are also handled as an average embedding of the known categories of this feature. See pure_example.py, feat_example.py for cold-start usage.