Collaboration, Reputation and Recommender Systems in Social Web Search

  title={Collaboration, Reputation and Recommender Systems in Social Web Search},
  author={Barry Smyth and Maurice Coyle and Peter Briggs and Kevin McNally and Michael P. O’Mahony},
  booktitle={Recommender Systems Handbook},
Modern web search engines have come to dominate how millions of people find the information that they are looking for online. While the sheer scale and success of the leading search engines is a testimony to the scientific and engineering progress that has been made over the last two decades, mainstream search is not without its challenges. Mainstream search engines continue to provide a largely one-size-fits-all service to their user-base, ultimately limiting the relevance of their result… 

Social Search

This chapter begins by framing the social search landscape in terms of the sources of data available and the ways in which this can be leveraged before, during, and after search.

Web Search Personalization Using Semantic Similarity Measure and Exploring Annotation based Web to Enhance Information Retrieval

This work implements a relevance model that guides the promotion of relevant results during regular Web search by storing the data on every users’ search activities: The queries they submit and results they select.

Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

The design of two intelligent optimization methods using Artificial Intelligence and Machine Learning for real-life applications that are used to improve the process of generation of recommenders are focused on.

Web Search Personalization Using Semantic Similarity Measure

This paper presents a relevance model to personalize search results which is based on query personalization, and proves that retrieving the search resultsbased on query modification is effective in providing the personalized results to the user.

Social Information Access

This chapter offers an introduction to the emerging field of social information access, a stream of research that explores methods for organizing the past interactions of users in a community in order to provide future users with better access to information.

Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications

It was experimentally found that the proposed cluster-based recommender improves the recommendation performance by resulting in more lessons completed when compared to learners present in the no-recommender cluster category.

Improving online learning of visual categories by deep features

This article presents an earlier developed system capable of real-time interactive visual category learning on a humanoid robot and shows that learning speed and performance can be strongly enhanced by employing a visual feature computation hierarchy based on deep learning.



Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine

It is argued that I-SPY strikes a useful balance between search personalization and user privacy, by offering a unique form of anonymous personalization, and in doing so may very well provide privacy-conscious Web users with an acceptable approach to personalized search.

Improving Web Search through Collaborative Query Recommendation

A novel query recommendation technique based on reusing previous search histories that is achieved by selecting, ranking, and then recommending previously successful queries to users and can lead to improved search performance based on live-user data.

Exploiting Extended Search Sessions for Recommending Search Experiences in the Social Web

This paper extends previous work that attempts to eliminate friction in HeyStaks by automatically recommending an active stak based on the searchers context and demonstrates significant improvements in stak recommendation accuracy.

A Novel Approach for Re-Ranking of Search Results Using Collaborative Filtering

  • U. RohiniVasudeva Varma
  • Computer Science
    2007 International Conference on Computing: Theory and Applications (ICCTA'07)
  • 2007
This paper proposes a novel approach for re-ranking of the search results using collaborative filtering techniques using relevance feedback of a given user as well as the other users.

Comparison-Based Recommendation

This paper describes and evaluates a novel comparison-based recommendation framework which is designed to utilise preference-based feedback, and presents results that highlight the benefits of a number of new query revision strategies and evidence to suggest that the popular more-like-this strategy may be flawed.

Google Shared. A Case-Study in Social Search

A novel and practical approach to Web search that combines ideas from personalization and social networking to provide a more collaborative search experience is described, which offers considerable business potential in a Google-dominated search marketplace.

Category ranking for personalized search

A Comparison of Machine Learning Techniques for Recommending Search Experiences in Social Search

The focus of this paper is to look at how machine learning techniques can be used to recommend a suitable active stak to the user at search time automatically.

Ontology based personalized search

  • A. PretschnerSusan Gauch
  • Computer Science
    Proceedings 11th International Conference on Tools with Artificial Intelligence
  • 1999
This paper explores ways of incorporating users' interests into the search process to improve the results and shows that fully automatic creation of large hierarchical user profiles is possible.

Trust in recommender systems

This paper proposes that the trustworthiness of users must be an important consideration in guiding recommendation and presents two computational models of trust and shows how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways.