From 42d891bd4d092d8e25e15ca4676fd063aef25051 Mon Sep 17 00:00:00 2001 From: guru4elephant <35550832+guru4elephant@users.noreply.github.com> Date: Sat, 27 Oct 2018 22:07:21 +0800 Subject: [PATCH] Update README.md --- fluid/PaddleRec/multiview-simnet/README.md | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/fluid/PaddleRec/multiview-simnet/README.md b/fluid/PaddleRec/multiview-simnet/README.md index e83ac70f..53d7c013 100644 --- a/fluid/PaddleRec/multiview-simnet/README.md +++ b/fluid/PaddleRec/multiview-simnet/README.md @@ -1,13 +1,13 @@ # Multi-view Simnet for Personalized recommendation ## Introduction -In personalized recommendation scenario, a user often is provided with serveral items from personalized interest matching model. In real world application, a user may have multiple views of features, say userid, age, click-history of items. A item, e.g. news, may also have multiple views of features like news title, news category and so on. Multi-view Simnet is matching a model that combine users' and items' multiple views of features into one unified model. The model can be used in many industrial product like baidu's feed news. +In personalized recommendation scenario, a user often is provided with several items from personalized interest matching model. In real world application, a user may have multiple views of features, say user-id, age, click-history of items. A item, e.g. news, may also have multiple views of features like news title, news category and so on. Multi-view Simnet is matching a model that combine users' and items' multiple views of features into one unified model. The model can be used in many industrial product like Baidu's feed news. ## Dataset Currently, synthetic dataset is provided for proof of concept and we aim to add more real world dataset in this project in the future. ## Model -This project aims to provide practical usage of Paddle in personalized matching scenario. The model provides serveral encoder modules for different views of features. Currenly, Bag-of-Embedding encoder, Temporal-Convolutional encoder, Gated-Recurrent-Unit encoder are provided. We will add more practical encoder for sparse features commonly used in recommender systems. Training algorithms used in this model is pairwise ranking in that a negative item with multiple views will be sampled given a pair of positive user-item pair. +This project aims to provide practical usage of Paddle in personalized matching scenario. The model provides several encoder modules for different views of features. Currently, Bag-of-Embedding encoder, Temporal-Convolutional encoder, Gated-Recurrent-Unit encoder are provided. We will add more practical encoder for sparse features commonly used in recommender systems. Training algorithms used in this model is pairwise ranking in that a negative item with multiple views will be sampled given a pair of positive user-item pair. ## Train The command line options for training can be listed by `python train.py -h` @@ -15,9 +15,6 @@ The command line options for training can be listed by `python train.py -h` python train.py ``` -## Infer -The command line options for inference can be listed by `python infer.py -h` - ## Future work - Multiple types of pairwise loss will be added in this project. For different views of features between a user and an item, multiple losses will be supported. The model will be verified in real world dataset. - infer will be added -- GitLab