What is Your Organization 'Like'?: A Study of Liking Activity in the
Enterprise
Workplace Social Performance
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Guy, Ido
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Ronen, Inbal
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Zwerdling, Naama
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Zuyev-Grabovitch, Irena
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Jacovi, Michal
Proceedings of the ACM CHI'16 Conference on Human Factors in Computing
Systems
2016-05-07
v.1
p.3025-3037
© Copyright 2016 ACM
Summary: The 'like' button, introduced by Facebook several years ago, has become one
of the most prominent icons of social media. Similarly to other popular social
media features on the web, enterprises have also recently adopted it. In this
paper, we present a first comprehensive study of liking activity in the
enterprise. We studied the logs of an enterprise social media platform within a
large global organization along a period of seven months, in which 393,720
'likes' were performed. In addition, we conducted a survey of 571 users of the
platform's 'like' button. Our evaluation combines quantitative and qualitative
analysis to inspect what employees like, why they use the 'like' button, and to
whom they give their 'likes'.
Social Media-Based Expertise Evidence
Papers
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Yogev, Arnon
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Guy, Ido
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Ronen, Inbal
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Zwerdling, Naama
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Barnea, Maya
Proceedings of the 14th European Conference on Computer-Supported
Cooperative Work
2015-09-19
p.63-82
© Copyright 2015 Springer International Publishing Switzerland
Summary: Social media provides a fertile ground for expertise location. The public
nature of the data supports expertise inference with little privacy
infringement and, in addition, presentation of direct and detailed evidence for
an expert's skillfulness in the queried topic. In this work, we study the use
of social media for expertise evidence. We conducted two user surveys of
enterprise social media users within a large global organization, in which
participants were asked to rate anonymous experts based on artificial and real
evidence originating from different types of social media data. Our results
indicate that the social media data types perceived most convincing as evidence
are not necessarily the ones from which expertise can be inferred most
precisely or effectively. We describe these results in detail and discuss
implications for designers and architects of expertise location systems.
Towards expressive exploratory search over entity-relationship data
Industry track presentations
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Yogev, Sivan
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Roitman, Haggai
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Carmel, David
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Zwerdling, Naama
Proceedings of the 2012 International Conference on the World Wide Web
2012-04-16
v.2
p.83-92
© Copyright 2012 ACM
Summary: In this paper we describe a novel approach for exploratory search over rich
entity-relationship data that utilizes a unique combination of expressive, yet
intuitive, query language, faceted search, and graph navigation. We describe an
extended faceted search solution which allows to index, search, and browse rich
entity-relationship data. We report experimental results of an evaluation
study, using a benchmark of several of entity-relationship datasets,
demonstrating that our exploratory approach is both effective and efficient
compared to other existing approaches.
Entity oriented search and exploration for cultural heritage collections:
the EU cultura project
European track presentations
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Carmel, David
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Zwerdling, Naama
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Yogev, Sivan
Proceedings of the 2012 International Conference on the World Wide Web
2012-04-16
v.2
p.227-230
© Copyright 2012 ACM
Summary: In this paper we describe an entity oriented search and exploration system
that we are developing for the EU Cultura project.
Social media recommendation based on people and tags
Filtering and recommendation
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Guy, Ido
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Zwerdling, Naama
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Ronen, Inbal
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Carmel, David
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Uziel, Erel
Proceedings of the 33rd Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2010-07-19
p.194-201
Keywords: collaborative tagging, personalization, recommender systems, social media,
social networks, social software
© Copyright 2010 ACM
Summary: We study personalized item recommendation within an enterprise social media
application suite that includes blogs, bookmarks, communities, wikis, and
shared files. Recommendations are based on two of the core elements of social
media -- people and tags. Relationship information among people, tags, and
items, is collected and aggregated across different sources within the
enterprise. Based on these aggregated relationships, the system recommends
items related to people and tags that are related to the user. Each recommended
item is accompanied by an explanation that includes the people and tags that
led to its recommendation, as well as their relationships with the user and the
item. We evaluated our recommender system through an extensive user study.
Results show a significantly better interest ratio for the tag-based
recommender than for the people-based recommender, and an even better
performance for a combined recommender. Tags applied on the user by other
people are found to be highly effective in representing that user's topics of
interest.
Personalized social search based on the user's social network
IR personalization and social search I
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Carmel, David
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Zwerdling, Naama
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Guy, Ido
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Ofek-Koifman, Shila
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Har'el, Nadav
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Ronen, Inbal
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Uziel, Erel
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Yogev, Sivan
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Chernov, Sergey
Proceedings of the 2009 ACM Conference on Information and Knowledge
Management
2009-11-02
p.1227-1236
© Copyright 2009 ACM
Summary: This work investigates personalized social search based on the user's social
relations -- search results are re-ranked according to their relations with
individuals in the user's social network. We study the effectiveness of several
social network types for personalization: (1) Familiarity-based network of
people related to the user through explicit familiarity connection; (2)
Similarity-based network of people "similar" to the user as reflected by their
social activity; (3) Overall network that provides both relationship types. For
comparison we also experiment with Topic-based personalization that is based on
the user's related terms, aggregated from several social applications. We
evaluate the contribution of the different personalization strategies by an
off-line study and by a user survey within our organization. In the off-line
study we apply bookmark-based evaluation, suggested recently, that exploits
data gathered from a social bookmarking system to evaluate personalized
retrieval. In the on-line study we analyze the feedback of 240 employees
exposed to the alternative personalization approaches. Our main results show
that both in the off-line study and in the user survey social network based
personalization significantly outperforms non-personalized social search.
Additionally, as reflected by the user survey, all three SN-based strategies
significantly outperform the Topic-based strategy.
Personalized recommendation of social software items based on social
relations
Tags and social networks
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Guy, Ido
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Zwerdling, Naama
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Carmel, David
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Ronen, Inbal
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Uziel, Erel
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Yogev, Sivan
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Ofek-Koifman, Shila
Proceedings of the 2009 ACM Conference on Recommender Systems
2009-10-23
p.53-60
© Copyright 2009 ACM
Summary: We study personalized recommendation of social software items, including
bookmarked web-pages, blog entries, and communities. We focus on
recommendations that are derived from the user's social network. Social network
information is collected and aggregated across different data sources within
our organization. At the core of our research is a comparison between
recommendations that are based on the user's familiarity network and his/her
similarity network. We also examine the effect of adding explanations to each
recommended item that show related people and their relationship to the user
and to the item. Evaluation, based on an extensive user survey with 290
participants and a field study including 90 users, indicates superiority of the
familiarity network as a basis for recommendations. In addition, an important
instant effect of explanations is found -- interest rate in recommended items
increases when explanations are provided.
Enhancing cluster labeling using wikipedia
Web 2.0
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Carmel, David
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Roitman, Haggai
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Zwerdling, Naama
Proceedings of the 32nd Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2009-07-19
p.139-146
Keywords: cluster labeling, wikipedia
© Copyright 2009 ACM
Summary: This work investigates cluster labeling enhancement by utilizing Wikipedia,
the free on-line encyclopedia. We describe a general framework for cluster
labeling that extracts candidate labels from Wikipedia in addition to important
terms that are extracted directly from the text. The "labeling quality" of each
candidate is then evaluated by several independent judges and the top evaluated
candidates are recommended for labeling.
Our experimental results reveal that the Wikipedia labels agree with manual
labels associated by humans to a cluster, much more than with significant terms
that are extracted directly from the text. We show that in most cases even when
human's associated label appears in the text, pure statistical methods have
difficulty in identifying them as good descriptors. Furthermore, our
experiments show that for more than 85% of the clusters in our test collection,
the manual label (or an inflection, or a synonym of it) appears in the top five
labels recommended by our system.
Social networks and discovery in the enterprise (SaND)
Demonstrations
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Ronen, Inbal
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Shahar, Elad
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Ur, Sigalit
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Uziel, Erel
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Yogev, Sivan
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Zwerdling, Naama
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Carmel, David
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Guy, Ido
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Har'el, Nadav
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Ofek-Koifman, Shila
Proceedings of the 32nd Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2009-07-19
p.836
Keywords: enterprise search, social network, social search
© Copyright 2009 ACM
On ranking techniques for desktop search
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Cohen, Sara
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Domshlak, Carmel
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Zwerdling, Naama
ACM Transactions on Information Systems
2008
v.26
n.2
p.11
© Copyright 2008 ACM
Summary: Users tend to store huge amounts of files, of various formats, on their
personal computers. As a result, finding a specific, desired file within the
file system is a challenging task. This article addresses the desktop search
problem by considering various techniques for ranking results of a search query
over the file system. First, basic ranking techniques, which are based on
various file features (e.g., file name, access date, file size, etc.), are
considered and their effectiveness is empirically analyzed. Next, two
learning-based ranking schemes are presented, and are shown to be significantly
more effective than the basic ranking methods. Finally, a novel ranking
technique, based on query selectiveness, is considered for use during the
cold-start period of the system. This method is also shown to be empirically
effective, even though it does not involve any learning.
On ranking techniques for desktop search
Search
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Cohen, Sara
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Domshlak, Carmel
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Zwerdling, Naama
Proceedings of the 2007 International Conference on the World Wide Web
2007-05-08
p.1183-1184
© Copyright 2007 International World Wide Web Conference Committee (IW3C2)
Summary: This paper addresses the desktop search problem by considering various
techniques for ranking results of a search query over the file system. First,
basic ranking techniques, which are based on a single file feature (e.g., file
name, file content, access date, etc.) are considered. Next, two learning-based
ranking schemes are presented, and are shown to be significantly more effective
than the basic ranking methods. Finally, a novel ranking technique, based on
query selectiveness is considered, for use during the cold-start period of the
system. This method is also shown to be empirically effective, even though it
does not involve any learning.
