SIR: Workshop on Social Interaction-based Recommendation

The 27th International Conference on Information and Knowledge Management (CIKM 2018) - October 22 2018, Turin (Italy)

Abstract:

The data collected in social media platforms has become an important source of information, usually exploited by a social recommender system to generate suggestions inside the platform. However, social interactions take many forms that go beyond what happens inside social media platforms, both online (e.g., chats) and offline (group activities performed together), and include “indirect'” forms of interactions, such as editing and reading collaborative resources. The aim of this workshop is to collect ideas on social interaction-based recommender systems, i.e., systems that in their processing consider the social interactions of the users in novel ways. The idea is to extend the classic notion of social recommendation, by using social interaction data to both produce suggestions inside the social media domain (e.g., recommending persons or social media contents, as in social recommenders) and to improve the existing recommendation technologies in other contexts (e.g., online news, online shopping, healthcare, etc.). The workshop will cover both the industrial and academic aspects of this research area, with keynote speakers and research papers from both sides, ending with a discussion that will try both to highlight the gap that still exists between the two and to create new collaborations.

Invited speaker:

TBD

Program:

TBD

Call for Papers:

Social recommender systems [2] 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) [1]. 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.

References:

[1] 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.

[2] 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.

Submission guidelines:

All papers must be formatted according to the ACM template format. Papers should be submitted in PDF format, electronically, using the CyberChair submission system, available at: CIKM-SIR@Cyberchair

Contacts:

For general enquires regarding the workshop, send an email to: sir@di.uniroma1.it