CIKM 2011 in Glasgow, UK, just closed its gates. Over 700 delegates spent a great time between research and fresh Haggis in Scotland’s co-capital.
For all those who could not attend, here is a short list of my personal highlights:
- Yuanhua Lv et al. – Lower-Bounding Term Frequency Normalization (Best Student Paper)
The authors argue that term frequency normalization may not always be optimal. Especially long documents are shown to be too strongly penalized by a number of well-known bag-of-words-based retrieval models.
- Maryam Karimzadehgan et al. – Improving Retrieval Accuracy of Difficult Queries through Generalizing Negative Document Language Models
Using information of negative examples (e.g., skipped documents on previous result pages) can be used to reorder the result list. In this work, the authors create a generalized model to capture undesired query facets which are subsequently demoted in the ranking.
- Katja Hofmann et al. – A probabilistic Method for Inferring Preferences from Clicks
The authors present a probabilistic approach to interleaving ranked result lists created by different ranking schemes. Based on click-through information, a learning to rank method is applied to infer the interleaving probabilities.
- Jiafeng Guo et al. – Intent-Aware Query Similarity (Best Paper)
The authors employ auxiliary information sources (search result snippets & click-through information) in order to determine query intent. Subsequently, surface feature-based query similarity measures can be augmented by search intent.
- Barbara Poblete et al. – Do all Birds Tweet the Same? Characterizing Twitter Around the World.
An interesting large-scale analysis of tweeting behaviour according to language families and countries of origin.
- Sergio Duarte Torres et al. – What and How Children Search on the Web
The authors analyze query logs of Yahoo’s US users to determine differences in the querying behaviour of different user age groups. The findings give quantitative evidence to hypotheses from cognitive science and children’s psychology.
- Kevyn Collins-Thompson et al. – Personalizing Web Search Results by Reading Level
The authors conduct a large-scale reading level analysis of web search engine results and the related snippets. They find that easy-to-read snippets leading to more difficult documents result in significantly shorter dwell times as the user’s expectation may be dissatisfied.
- Dimitrios Lymberopoulos et al. – Location-Aware Click Prediction in Mobile Local Search
The authors analyze location-aware web searches originating from local devices. They find significant differences in the locality preferences of inhabitants of different US states. While some states are more primarily interested in local resources, others are willing to travel further. Adjusting for said preferences was able to improve ranking quality.