Prof. Gabriella Pasi is Full Professor at the University of Milano-Bicocca, Department of Informatics, Systems, and Communication (DISCo).
(Italy) and she obtained her Ph.D. in Computer Sciences at the University of Rennes (France).
Since 2005 she leads the Information Retrieval Laboratory (IR LAB) at the Department of Informatics, Systems, and Communication (DISCo).
From 2013 to 2017 she has been president of the European Association on Fuzzy Logic and Technologies (EUSFLAT).
The research activity of Gabriella Pasi is finalised at defining innovative models and systems that allow an effective access to digital information relevant to specific user needs. The considered systems include Information Retrieval and Filtering Systems, Database Management Systems and Decision Support Systems.
Profile: Gabriella Pasi @ Milano-Bicocca
Contact her at firstname.lastname@example.org
Issues and Challenges of the Social Aspects of Personalization
This talk will focus on the role of social media in personalization, in particular related to the tasks of search and of recommendation.
Some possibile ways in which the content generated by users can be leveraged to define users’ models will be outlined, by pointing out issues and challenges.
09:00 - 10:40 - Session 1:
09:00 - 09:05 - Welcome Message - Opening remarks
- 09:05 - 09:25 - Query Embedding Learning for Context-based Social Search
- 09:25 - 09:45 - AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments
- 09:45 - 10:15 - Neural Educational Recommendation Engine (NERE)
- 10:15 - 10:40 - A Framework for building Chat-based Recommender Systems
10:40 - 11.10 - Coffee break
11:10 - 12:30 - Session 2:
11:10 - 11:50 - Keynote - Issues and Challenges of the Social Aspects of Personalization
- 11:50 - 12:15 - Constructing CP-nets from Users Past Behaviors
- 12:15 - 12:30 - The challenge of personal attribute preferences in recommending diverse, reliable news sources
12:30 - 13:30 - Lunch
13:30 - 15:00 - Session 3:
- 13:30 - 13:45 - A Crowd-powered Model for Identifying Negative Citations
- 13:45 - 14:00 - A Virtual Teaching Assistant for Personalized Learning
- 14:00 - 14:25 - A User Experience Model for Privacy and Context Aware Over-the-Top (OTT) TV Recommendations
14:25 - 14:50 - Open discussion: SIR 2018 Proceedings and Special Issue, and SIR 2019
14:50 - 15:00 - Closing remarks
Call for Papers:
Social recommender systems  aim at performing suggestions in social media platforms, by exploiting the information collected during the interaction of the users, both with the platform (e.g., tags, likes, and comments) and among themselves (i.e., the social network).
However, the social interactions of the users can also be employed in richer ways, both inside a social media platform and in classic recommender systems that do not operate in the social media domain (e.g., in collaborative and content-based approaches). With the term social interaction-based recommendation we identify a novel class of systems that exploits social interactions, in order to provide recommendations to the users (individuals or groups), either inside a social media platform or in classic recommender systems, both online and offline.
Therefore, while social recommender systems remain an important part of this workshop, the social interactions of the users can also be exploited in other domains. Indeed, a new wave of research is trying to learn ratings from textual comments (e.g., reviews) . Moreover, the analysis of the interactions of two or more users in chats or private messages, leads to novel forms of knowledge on the shared preferences between these users, which can be exploited in any kind of recommender system.
Social interaction information can also be used offline, e.g., to recommend social events that an individual could attend, or to perform group recommendations of activities/items that a group of users could do/consume together.
The aim of this workshop is to collect novel ideas for approaches that use any form of social interaction to improve existing recommendation technologies. Indeed, we solicit contributions in all topics related to employing social interaction information to perform recommendations, focused (but not limited) to the following list:
- Chat-based recommender systems;
- Social recommender systems;
- Group recommender systems;
- Semantic technologies to exploit social media comments in recommender systems;
- Integrating information collected in social media in other types of recommender systems;
- Integrating information collected outside social media (e.g., ratings) in social recommender systems;
- Modeling user's social behavior for recommendation;
- Hybrid systems that combine a social component with classic recommendation strategies.
 L. Chen, G. Chen, and F. Wang. Recommender systems based on user reviews: the state of the art. "User Model. User-Adapt. Interact.", 25(2):99–154, 2015.
 I. Guy. Social recommender systems. In "Recommender Systems Handbook", pages 511–543. Springer, 2015.
Types of contributions:
We will consider three different submission types, all in the ACM template format: regular (8 pages), short (4 pages) and extended abstracts (2 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.
Insights and results papers (short) should provide a presentation of ideas and insights, along with the results that validate these ideas, to have quick and inspiring exchanges among the workshop attendants. The “insights and results” papers will be presented in a novel and dedicated “Dedicated Session” (inspired by the location of the workshop), aimed at stimulating these exchanges.
Practice and experience reports (short) should present in detail the real-world scenarios in which social information is employed to perform recommendations.
Demo proposals (extended abstract) should present the details of a prototype or complete system, to be demonstrated to the workshop attendees.
For general enquires regarding the workshop, send an email to: email@example.com