massquantity

For example, suppose the `train_data` has original item ids: `1, 3, 5, 7, 9`, and the `test_data` has original item ids: `2, 3`. In LibRecommender, item 2 will be excluded...

The mappings for train_data are all stored in `data_info`. `train_data, data_info = DatasetFeat.build_trainset(train_data, user_col, item_col, sparse_col, dense_col)`. `data_info.user2id` is a `dict`, which maps original user ids to mapped ids, and...

When making recommendation for a user, we compute prediction scores for every candidate item and rank them according to the corresponding scores, at least in this library. So I think...

Yes it is possible to achieve this for models that generate item embeddings such as SVD, BPR, ALS, Item2Vec, Caser etc, but different models have different ways. So maybe you...

It would be difficult for DeepFM to achieve that. Some tricky methods might exist, but performance is not guaranteed. So I would choose item2vec too, since it is designed for...

Your observation is correct, and it's a mistake that `recommend_user` function doesn't convert item_ids. Actually the `KnnEmbedding` class where `sort_topk_items` lines in is under development and I plan to implement...

The logic in `predict` and `recomender_user` is different. In `predict`, the model computes the similarities between target item 109 and items that the user has interacted, and returns the mean...

For algorithms such as Caser , SVD, ALS, BPR, you can use item embeddings generated by the model and perform some similarity metrics such as dot product to get top...

In [`_set_last_interacted`](https://github.com/massquantity/LibRecommender/blob/master/libreco/algorithms/caser.py#L328), [` _set_latent_factors`](https://github.com/massquantity/LibRecommender/blob/master/libreco/algorithms/caser.py#L345) and [`user_last_interacted`](https://github.com/massquantity/LibRecommender/blob/master/libreco/data/sequence.py#L121:5) functions, the information of last L items sequence has already been incorporated in the final `self.user_vector` and `self.item_vector`, which are used to make predictions...

Yeah i was thinking about the same thing when implementing these sequence models, but this would invoke a lot of complexity and MAYBE in the far future i will add...