Groups can be built intentionally by an explicit definition from the users Smith et al. For instance, in the scenario of recommending recipes to a group of family members, because of the seafood allergy of one family member, recipes including shrimp or sea-crab might not be recommended to the whole group.
There are still open issues on group decision making which need to be resolved in the future research, such as bundle recommendations, intelligent user interface design, group aggregation strategies for cold-start problems Masthoffconsensus achievement within group members, and counteracting decision biases in group decision processes Felfernig et al.
So we should only compare the relative difference between these scores instead of their magnitude. Type 3 generates recommendations on the basis of considering both above criteria for the purpose of balancing between the food users like and the food users should consume. Model Evaluation a.
In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence pp.
Since LightFM normalizes each row in the item features to have its sum to 1, some of the features such as review count, which have very large values, could potentially dominate the weight assigned.
Logistic loss seems to perform worse when metadata is included while all learning to rank method performs better in a hybrid setting. On the other hand, if user A likes vegetarian places, businesses serving vegetarian choices will be similar to each other.
While these are great introductory exercises, they fall short of real-life examples.