**Random Walks on the Click Graph, SIGIR 07 by MSR Cambridge.**

This paper generally describes that random walk is effective in smoothing the noisy and sparse click log data. One thing makes me confusing is the “self-transition probability” (in Section 3.1), which, I think, could be renamed as “exiting probability” as in PageRank for intuition.

**Optimizing Web Search Using Web Click-through Data, CIKM ’04 by MSR Asia.**

This paper does not follow random walk naively; instead, it considers co-citation relationship between queries (as well between urls), and proposed an iterative algorithm working with such co-citation relationship. However, there isn’t a consistent math model for this algorithm. I am wondering maybe we can derive the method by random walking on the co-citation graph (instead of the query-url bipartite graph).

**Click-Through Rate Estimation for Rare Events in Online Advertising, by Yahoo! Labs.**

In this paper, two methods were visited: (1) learning an optimal beta prior (or beta smoothing factor) by considering clicking as a Bernoulli experiment, and (2) the well-known exponential smoothing method.

**Smoothing Click-through Data for Web Search Ranking, SIGIR’09 by MSR Redmond**

This paper is about smoothing click-through data for feature construction in learning-to-rank. Two methods were proposed: (1) random walk, and (2) a discounting method inspired by the Good-Turing method used in statistical language model.

**Using the Wisdom of the Crowds for Keyword Generation, WWW’08 by MSR Mountain View**

This paper describes a modified random walk algorithm to categorize queries (via urls) to pre-defined “concepts”. The algorithm is bootstrapped by seeding each concept some urls. And if concepts (or categories) are mutual exclusive, the algorithm can be accelerated.

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