Fabrizio Silvestri is currently a software engineer at Facebook working in the search team on topics related to query log mining.
Prior to Facebook, Fabrizio was a principal scientist at Yahoo where he has worked on sponsored search and native ads within the Gemini project.
Fabrizio holds a Ph.D. in Computer Science from the University of Pisa, Italy where he studied problems related to Web Information Retrieval with particular focus on Efficiency related problems like Caching, Collection Partitioning, and Distributed IR in general.
He has been the recipient (together with Ranieri Baraglia) of the best Web Intelligence 2004 paper award, and the recipient of the ECIR 06 best paper award.
In 2014 he has been a recipient of the best paper award at the internal Yahoo conference: Tech Pulse. He is the author of more than 130 papers and he has patents filed in the area of web advertising.
Contact him at firstname.lastname@example.org
On The Use of Embeddings in Search and Personalization Applications
One of the main innovations rediscovered in the last years in search and machine learning is the concept of Embeddings.
After Google proposed Word2Vec in 2013 there have been a proliferation of applications that make use of embeddings to improve the quality of their solutions.
In search, embeddings have been used in many different applications including retrieval, advertising, and recommender systems. In this talk we are going to show some applications of vector space embeddings that have considerably improved the state of the art.
All the applications shown have been adopted by main search companies in production in their systems.
Call for Papers:
In order to improve the web experience of the users, classic personalization technologies (e.g., recommender systems) and search engines usually rely on static schemes. Indeed, users are allowed to express ratings in a fixed range of values for a given catalogue of products, or to express a query that usually returns the same set of webpages/products for all the users.
With the advent of social media, users have been allowed to create new content and to express opinions and preferences through likes and textual comments. Moreover, the social network itself can provide information on who influences who. Being able to mine usage and collaboration patterns in social media and to analyze the content generated by the users opens new frontiers in the generation of personalization services and in the improvement of search engines. Moreover, recent technological advances, such as deep learning, are able to provide a context to the analyzed data (e.g., Google’s word2vec provides a vector representation of the words in a corpus, considering the context in which a word has been used).
Our workshop will solicit contributions in all topics related to employing social media for personalization and search purposes, focused (but not limited) to the following list:
- Recommender systems;
- Search and tagging;
- Query expansion;
- User modeling and profiling;
- Advertising and ad targeting;
- Content classification, categorization, and clustering;
- Using social network features/community detection algorithms for personalization and search purposes.
All accepted papers will be made available on the workshop website together with the material generated during the meeting. The SoMePeAS 2017 Workshop proceedings will also be available in the CEUR series, and indexed on DBLP and Scopus. Authors of selected papers will be invited to submit an extended version in a journal special issue.
Types of contribution:
We will consider three different submission types, all in the LNCS format: regular (12 pages), short (6 pages) and extended abstracts (2-4 pages).
Research and position papers (regular or short) should be clearly placed with respect to the state of the art and state the contribution of the proposal in the domain of application, even if presenting preliminary results. In particular, research papers should describe the methodology in detail, experiments should be repeatable, and a comparison with the existing approaches in the literature should be made where possible.
Position papers(short) should introduce novel point of views in the workshop topics or summarize the experience of a researcher or a group in the field.
Practice and experience reports (short) should present in detail the real-world scenarios in which social media is employed for personalization and search purposes.
Demo proposals (extended abstract) should present the details of a prototype or complete application that employs social media is employed for personalization and search purposes. The systems will be demonstrated to the workshop attendees.
The reviewing process will be coordinated by the organizers. Each paper will receive three reviews: two externals to the organizing committee and one internal. The external reviewers will be contacted according to their expertise in the paper topic.
All submission must be written in English and follow the ECIR paper guidelines. All papers must be formatted according to the LNCS format style. Papers should be submitted in PDF format, electronically, using the EasyChair submission system, available at: SoMePeAS@EasyChair
For general enquires regarding the workshop, send an email to: email@example.com