63,361 research outputs found
A probabilistic model to resolve diversity-accuracy challenge of recommendation systems
Recommendation systems have wide-spread applications in both academia and
industry. Traditionally, performance of recommendation systems has been
measured by their precision. By introducing novelty and diversity as key
qualities in recommender systems, recently increasing attention has been
focused on this topic. Precision and novelty of recommendation are not in the
same direction, and practical systems should make a trade-off between these two
quantities. Thus, it is an important feature of a recommender system to make it
possible to adjust diversity and accuracy of the recommendations by tuning the
model. In this paper, we introduce a probabilistic structure to resolve the
diversity-accuracy dilemma in recommender systems. We propose a hybrid model
with adjustable level of diversity and precision such that one can perform this
by tuning a single parameter. The proposed recommendation model consists of two
models: one for maximization of the accuracy and the other one for
specification of the recommendation list to tastes of users. Our experiments on
two real datasets show the functionality of the model in resolving
accuracy-diversity dilemma and outperformance of the model over other classic
models. The proposed method could be extensively applied to real commercial
systems due to its low computational complexity and significant performance.Comment: 19 pages, 5 figure
A Distributed Method for Trust-Aware Recommendation in Social Networks
This paper contains the details of a distributed trust-aware recommendation
system. Trust-base recommenders have received a lot of attention recently. The
main aim of trust-based recommendation is to deal the problems in traditional
Collaborative Filtering recommenders. These problems include cold start users,
vulnerability to attacks, etc.. Our proposed method is a distributed approach
and can be easily deployed on social networks or real life networks such as
sensor networks or peer to peer networks
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback
Albeit, the implicit feedback based recommendation problem - when only the
user history is available but there are no ratings - is the most typical
setting in real-world applications, it is much less researched than the
explicit feedback case. State-of-the-art algorithms that are efficient on the
explicit case cannot be straightforwardly transformed to the implicit case if
scalability should be maintained. There are few if any implicit feedback
benchmark datasets, therefore new ideas are usually experimented on explicit
benchmarks. In this paper, we propose a generic context-aware implicit feedback
recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor
factorization learning method that scales linearly with the number of non-zero
elements in the tensor. The method also allows us to incorporate diverse
context information into the model while maintaining its computational
efficiency. In particular, we present two such context-aware implementation
variants of iTALS. The first incorporates seasonality and enables to
distinguish user behavior in different time intervals. The other views the user
history as sequential information and has the ability to recognize usage
pattern typical to certain group of items, e.g. to automatically tell apart
product types or categories that are typically purchased repetitively
(collectibles, grocery goods) or once (household appliances). Experiments
performed on three implicit datasets (two proprietary ones and an implicit
variant of the Netflix dataset) show that by integrating context-aware
information with our factorization framework into the state-of-the-art implicit
recommender algorithm the recommendation quality improves significantly.Comment: Accepted for ECML/PKDD 2012, presented on 25th September 2012,
Bristol, U
Advanced recommendations in a mobile tourist information system
An advanced tourist information provider system delivers information regarding sights and events on their users' travel route. In order to give sophisticated personalized information about tourist attractions to their users, the system is required to consider base data which are user preferences defined in their user profiles, user context, sights context, user travel history as well as their feedback given to the sighs they have visited. In
addition to sights information, recommendation on sights to the user could also be provided. This project concentrates on combinations of knowledge on recommendation systems and base information given by the users to build a recommendation component in the Tourist Information Provider or TIP system. To accomplish our goal, we not only examine several tourist information systems but also conduct the investigation on recommendation systems. We propose a number of approaches for advanced recommendation models in a tourist information system and select a subset of these for implementation to prove the concept
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