Workshop Scope ==================== As the web information exponentially grows and the needs of users become more specific, traditional general web search engines are not able to perfectly satisfy the nowadays user requirement. Vertical search engines have emerged in various domains, which more focus on specific segments of online content, including local, shopping, medical information, travel search, etc. Designing effective ranking functions for vertical search has become practically important to improve users' experience in both web search and vertical search.
Challenging research issues in the field of relevance for vertical search mainly fall into two categories. The first category includes how to learn an effective ranking model considering multi-facet relevance (e.g., text relevance, distance, ratings, and prices): (1) integrating multiple aspect relevance: vertical search engines need to consider and tradeoff the relevance from different aspects before making the overall relevance judgment; (2) query-dependent multiple aspects: such a tradeoff can vary for different queries or in different contexts (e.g., searching a chain-store like CVS is more location-sensitive than a specific restaurant); (3) the involved aspects and the tradeoff among them are vertical-dependent. Collecting training data with overall relevance for a new vertical requires human editors learn how to appropriately tradeoff different aspects. The second category focuses on building effective business model in the context of specific vertical search systems. Comparing with the business model in web search, some vertical search engines (e.g., shopping and travel) are naturally business-oriented, which enables flexible business models into ranking with awareness of relevance of original queries.
The workshop will bring together researchers from IR, ML, NLP, and other areas of computer and information science, who are working on or interested in this area. It provides a forum for the researchers to identify the issues and the challenges, to share their latest research results, to express a diverse range of opinions about this topic, and to discuss future directions.
Submissions ==================== Papers must be formatted for US Letter size according to ACM guidelines and style files, must fit within 10 pages (with a font size no smaller than 9pt), including references, diagrams, and appendices if any. A submitted paper must be self-contained and in English. Papers must be submitted in PDF format to the Easychair Web site (https://easychair.org/conferences/?conf=vsr2015).
Topics of Interests ==================== Topics of interests include, but are not limited to: - Vertical domain content representation and analysis - Vertical domain query analysis and modeling - Retrieval models and ranking - Architectures and scalability of vertical search engine - Users and interactive IR in vertical search - Evaluation for vertical search - Business model for vertical search - Applications (e.g., digital libraries, enterprise search, commercial search, genomics IR, legal IR, patent search, etc.)