Domain-specific queries and Web search personalization: some investigations

@inproceedings{Hoang2015DomainspecificQA,
  title={Domain-specific queries and Web search personalization: some investigations},
  author={Van Tien Hoang and Angelo Spognardi and Francesco Tiezzi and Marinella Petrocchi and Rocco De Nicola},
  booktitle={WWV},
  year={2015}
}
Major search engines deploy personalized Web results to enhance users' experience, by showing them data supposed to be relevant to their interests. Even if this process may bring benefits to users while browsing, it also raises concerns on the selection of the search results. In particular, users may be unknowingly trapped by search engines in protective information bubbles, called "filter bubbles", which can have the undesired effect of separating users from information that does not fit their… 

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References

SHOWING 1-10 OF 12 REFERENCES

Measuring personalization of web search

TLDR
A methodology for measuring personalization in Web search results is developed and it is found that, on average, 11.7% of results show differences due to personalization, but that this varies widely by search query and by result ranking.

A novel approach to personalize web search through user profiling and query reformulation

TLDR
A novel approach is proposed that personalize web search result through query reformulation and user profiling that identifies relevant search term for particular user from previous search history by analysing web log file maintained in the server.

Mining user context based on interactive computing for personalized Web search

  • Jie YuFangfang Liu
  • Computer Science
    2010 2nd International Conference on Computer Engineering and Technology
  • 2010
TLDR
This paper introduces an approach that captures the user context to accurately provide preferences of users for effective personalized search and demonstrates that this approach can successfully build user context according to individual user information need.

Personalization of web-search using short-term browsing context

TLDR
This is the first study addressing the problem of short-term personalization based on recent browsing history and it is found that performance of this re-ranking approach can be reasonably predicted given a query.

A large-scale evaluation and analysis of personalized search strategies

TLDR
It is revealed that personalized search has significant improvement over common web search on some queries but it also has little effect on other queries, and even harms search accuracy under some situations.

Implicitly Learning a User Interest Profile for Personalization of Web Search Using Collaborative Filtering

TLDR
A robust user modeling technique is proposed that implicitly creates a Dynamic Category Interest Tree (DCIT), using a general ontology of the web and a set of web pages collected over time that give an insight into a user's interests.

Predicting short-term interests using activity-based search context

TLDR
This study developed and evaluated user interest models for the current query, its context (from pre-query session activity), and their combination, which is referred to as intent, and investigates optimally combining the query and its context by learning a model that predicts the context weight for each query.

Predictive client-side profiles for personalized advertising

TLDR
Experiments demonstrate that predictive client-side personalization allows ad platforms to retain almost all of the revenue gains from personalization even if they give users the freedom to opt out of behavior tracking backed by server-side storage.

Modeling search processes using hidden states in collaborative exploratory web search

TLDR
This paper proposed an innovative approach to model collaborative search processes using Hidden Markov Model (HMM), which is an automatic technique for analyzing temporal sequential data, and showed that the identified hidden patterns of search process through HMM are compatible with previous well-known models.

The Filter Bubble: What the Internet Is Hiding from You

Author Q&A with Eli Pariser Q: What is a Filter Bubble? A: Were used to thinking of the Internet like an enormous library, with services like Google providing a universal map. But thats no longer