39,839 research outputs found
Exploiting Synergy Between Ontologies and Recommender Systems
Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured
Musical recommendations and personalization in a social network
This paper presents a set of algorithms used for music recommendations and
personalization in a general purpose social network www.ok.ru, the second
largest social network in the CIS visited by more then 40 millions users per
day. In addition to classical recommendation features like "recommend a
sequence" and "find similar items" the paper describes novel algorithms for
construction of context aware recommendations, personalization of the service,
handling of the cold-start problem, and more. All algorithms described in the
paper are working on-line and are able to detect and address changes in the
user's behavior and needs in the real time.
The core component of the algorithms is a taste graph containing information
about different entities (users, tracks, artists, etc.) and relations between
them (for example, user A likes song B with certainty X, track B created by
artist C, artist C is similar to artist D with certainty Y and so on). Using
the graph it is possible to select tracks a user would most probably like, to
arrange them in a way that they match each other well, to estimate which items
from a fixed list are most relevant for the user, and more.
In addition, the paper describes the approach used to estimate algorithms
efficiency and analyze the impact of different recommendation related features
on the users' behavior and overall activity at the service.Comment: This is a full version of a 4 pages article published at ACM RecSys
201
The SECURE collaboration model
The SECURE project has shown how trust can be made computationally tractable while retaining a reasonable connection with human and social notions of trust. SECURE has produced a well-founded theory of trust that has been tested and refined through use in real software such as collaborative spam filtering and electronic purse. The software comprises the SECURE kernel with extensions for policy specification by application developers. It has yet to be applied to large-scale, multi-domain distributed systems taking different application contexts into account. The project has not considered privacy in evidence distribution, a crucial issue for many application domains, including public services such as healthcare and police. The SECURE collaboration model has similarities with the trust domain concept, embodying the interaction set of a principal, but SECURE is primarily concerned with pseudonymous entities rather than domain-structured systems
Exploiting synergy between ontologies and recommender systems
Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations.Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain.
This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured
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Privacy-preserving scheme for mobile ad hoc networks.
This paper proposes a decentralized trust establishment protocol for mobile ad hoc networks (MANETs), where nodes establish security associations. In order to achieve privacy and security, we use homomorphic encryption and polynomial intersection so as to find the intersection of two sets. The first set represents a list of recommenders of the initiator and the second set is a list of trusted recommenders of the responder. The intersection of the sets represents a list of nodes that recommend the first node and their recommendations are trusted by the second node. In our experimental results we show that our scheme is effective even if there are 30 trusted nodes
Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation
With the uptake of algorithmic personalization in the news domain, news
organizations increasingly trust automated systems with previously considered
editorial responsibilities, e.g., prioritizing news to readers. In this paper
we study an automated news recommender system in the context of a news
organization's editorial values. We conduct and present two online studies with
a news recommender system, which span one and a half months and involve over
1,200 users. In our first study we explore how our news recommender steers
reading behavior in the context of editorial values such as serendipity,
dynamism, diversity, and coverage. Next, we present an intervention study where
we extend our news recommender to steer our readers to more dynamic reading
behavior. We find that (i) our recommender system yields more diverse reading
behavior and yields a higher coverage of articles compared to non-personalized
editorial rankings, and (ii) we can successfully incorporate dynamism in our
recommender system as a re-ranking method, effectively steering our readers to
more dynamic articles without hurting our recommender system's accuracy.Comment: To appear in UMAP 202
A Review and Characterization of Progressive Visual Analytics
Progressive Visual Analytics (PVA) has gained increasing attention over the past years.
It brings the user into the loop during otherwise long-running and non-transparent computations
by producing intermediate partial results. These partial results can be shown to the user
for early and continuous interaction with the emerging end result even while it is still being
computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth
various interpretations and instantiations that have created a research domain of competing terms,
various definitions, as well as long lists of practical requirements and design guidelines spread across
different scientific communities. This makes it more and more difficult to get a succinct understanding
of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and
discussion of PVA presented in this paper address these issues and provide (1) a literature collection
on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical
recommendations for implementing and using PVA-based visual analytics solutions
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