Social Search and applications based on mining logs of web activity are gaining momentum after the rapid growth of web 2.0. This rapid expansion has converted the Web into a multitude of user-generated applications, which in turn generate a very large and noisy dataset of logs. These datasets represent an extremely valuable source of information, strategic to augment marketing, business intelligence and to enhance information retrieval via social search engines.

Modern search systems, as well as online marketing and web-analytics applications can benefit greatly from using additional information about user's behaviour. Implicit or explicit feedback data based on direct interaction (e.g., clicks, scrolling, etc.), as well as user profiles/preferences, have proven a valuable source for enhancing and personalizing information retrieval and other web-related applications, such as customer segmentation and marketing. One of the main challenges remains how to effectively mine a large set of complex data affected by great level of noise, represented by non-pertinent, untrustworthy or even malicious data. Moreover, the challenge is to define models of users’ behaviour that can resists malicious attack, low quality information and preserve privacy.
Trust and Recommender systems, along with personalization techniques, and models of users’ behaviour appear to be essential candidates to enhance and support an effective analysis of web activity. These mechanisms could help filter, interpret and rank web-users' behaviour to assign the relevance of web-search results and deliver the most reliable and adequate content. Similarly, they may be helpful to define user-based anti-spam techniques, support web-analytics applications that mine only trustworthy sites and users’ activity, help users’ segmentation and decisions support tools for online marketing. This workshop focuses on discussing and identifying the most promising research directions with respect to applying reputation, trust systems and implicit or explicit users’ models in the analysis and exploitation of web-activity logs.
The focus of the workshops are innovative and challenging applications of recommendation and trust systems, personalization techniques, users models and data mining in the field of social search, user analysis and understanding. Particular interests are the following challenges:

* How to built implicit/explicit models to rank and filter information based on users activity analysis
* How trust and recommender systems can be used to enhance web logs analysis and social search engine
* Users models resistant to attack and privacy-aware
* Implicit users or trust models based on web-logs mining

Topics

The workshop brings together researchers from Recommender/Trust Systems as well as Data Mining, Multi-agent Systems, Information Retrieval and Human Computer Interaction. Topics of interest include, but are not limited to:
* Trust, Reputation and Recommender systems for Social Search
* Trust and Reputation based on User Behavioural Analysis
* Reasoning and Data-Mining approach to trust and reputation using Web-logs
* Anonymity and privacy-aware solutions for Social Search Systems
* Users profiling and personalization in Social Search and Web-analytics
* Cognitive models of users on the Web
* Mining and Learning reputation and Users behaviour on the Web
* Applications of recommender and trust systems to web analytics, information retrieval, marketing analysis, social search
* Web Information Filtering and Retrieval in social networks and multi-agent systems
* Web Security, Integrity, Privacy and Trust
* Agent Systems Modelling and Methodology
* Autonomous Knowledge and Information Agents (web entities)
* Distributed Intelligence and Multi-agent paradigms for Social Search