# 参考文献

IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

Information retrieval GAN

IRGAN SIGIR paper experimental code

# 关键问题

1. 研究对象？

Information retrieval(IR) is to provide a list of documents(e.g., textual documents, information items, answers) given a query. There are two major schools of thinking when coming to IR theory and modeling.

Generative school: focused on describing how a document is generated from a given information need: q → d.

Discriminative(Classification) school: Consider documents and queries jointly as features and predicts their relevancy from a large amount of training data: q + d → r.

2. 生成式检索模型？

$p_θ (d|q, r)$ tries to generate (or select) relevant documents from the candidate pool for the given query $q$, whose goal is to approximate the true relevance distribution over documents $p_{true} (d|q, r)$ as much as possible.

在推荐系统中，作者使用的是矩阵分解，即下图中的函数$g$是右侧的函数$s$。

3. 判别式检索模型？

$f_ϕ (q,d)$ tries to discriminate well-matched query-document tuples $(q,d)$ from ill-matched ones, which is in fact simply a binary classifier, and we could use 1 for truly match pair while 0 for those that do not really match.

4. 总体框架？

$p_θ (d|q, r)$ tries to approximate the true relevance distribution over documents $p_{true} (d|q, r)$ as much as possible.

$f_ϕ (q,d)$ tries to discriminate well-matched query-document tuples (q,d) from ill-matched ones.

5. 推广到pair-wise的情况？

we have a set of labeled document pairs $R_n=\{│d_i≻d_j \}$. The generator $G$ would try to generate document pairs that are similar to those in $R_n$, i.e., the correct ranking. The discriminator $D$ would try to distinguish such generated document pairs from those real document pairs.

6. 优化算法？

判别器使用的是随机梯度下降，而生成器为了处理自然语言处理中的问题，所以使用的策略梯度下降：