Modeling Attractiveness and Multiple Clicks in Sponsored Search Results
arXiv:1401.0255
Abstract
Click models are an important tool for leveraging user feedback, and are used by commercial search engines for surfacing relevant search results. However, existing click models are lacking in two aspects. First, they do not share information across search results when computing attractiveness. Second, they assume that users interact with the search results sequentially. Based on our analysis of the click logs of a commercial search engine, we observe that the sequential scan assumption does not always hold, especially for sponsored search results. To overcome the above two limitations, we propose a new click model. Our key insight is that sharing information across search results helps in identifying important words or key-phrases which can then be used to accurately compute attractiveness of a search result. Furthermore, we argue that the click probability of a position as well as its attractiveness changes during a user session and depends on the user's past click experience. Our model seamlessly incorporates the effect of externalities (quality of other search results displayed in response to a user query), user fatigue, as well as pre and post-click relevance of a sponsored search result. We propose an efficient one-pass inference scheme and empirically evaluate the performance of our model via extensive experiments using the click logs of a large commercial search engine.
Cited by in corpus (8)
- Colors Messengers of Concepts: Visual Design Mining for Learning Color Semantics
- Comparing Face Detection and Recognition Techniques
- Personalized Attraction Enhanced Sponsored Search with Multi-task Learning
- Finding Optimal Combination of Kernels using Genetic Programming
- Can Boosting with SVM as Week Learners Help?
- Accessing accurate documents by mining auxiliary document information
- Thesis: Multiple Kernel Learning for Object Categorization
- System for Filtering Messages on Social Media Content