121,120 research outputs found
Evaluating the effectiveness of explanations for recommender systems : Methodological issues and empirical studies on the impact of personalization
Peer reviewedPostprin
Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations
Collaborative filtering algorithms haven been widely used in recommender
systems. However, they often suffer from the data sparsity and cold start
problems. With the increasing popularity of social media, these problems may be
solved by using social-based recommendation. Social-based recommendation, as an
emerging research area, uses social information to help mitigate the data
sparsity and cold start problems, and it has been demonstrated that the
social-based recommendation algorithms can efficiently improve the
recommendation performance. However, few of the existing algorithms have
considered using multiple types of relations within one social network. In this
paper, we investigate the social-based recommendation algorithms on
heterogeneous social networks and proposed Hete-CF, a Social Collaborative
Filtering algorithm using heterogeneous relations. Distinct from the exiting
methods, Hete-CF can effectively utilize multiple types of relations in a
heterogeneous social network. In addition, Hete-CF is a general approach and
can be used in arbitrary social networks, including event based social
networks, location based social networks, and any other types of heterogeneous
information networks associated with social information. The experimental
results on two real-world data sets, DBLP (a typical heterogeneous information
network) and Meetup (a typical event based social network) show the
effectiveness and efficiency of our algorithm
Personality in Computational Advertising: A Benchmark
In the last decade, new ways of shopping online have increased the
possibility of buying products and services more easily and faster
than ever. In this new context, personality is a key determinant
in the decision making of the consumer when shopping. A personās
buying choices are influenced by psychological factors like
impulsiveness; indeed some consumers may be more susceptible
to making impulse purchases than others. Since affective metadata
are more closely related to the userās experience than generic
parameters, accurate predictions reveal important aspects of userās
attitudes, social life, including attitude of others and social identity.
This work proposes a highly innovative research that uses a personality
perspective to determine the unique associations among the
consumerās buying tendency and advert recommendations. In fact,
the lack of a publicly available benchmark for computational advertising
do not allow both the exploration of this intriguing research
direction and the evaluation of recent algorithms. We present the
ADS Dataset, a publicly available benchmark consisting of 300 real
advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated
by 120 unacquainted individuals, enriched with Big-Five usersā
personality factors and 1,200 personal usersā pictures
Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations
Latent factor models for recommender systems represent users and items as low
dimensional vectors. Privacy risks of such systems have previously been studied
mostly in the context of recovery of personal information in the form of usage
records from the training data. However, the user representations themselves
may be used together with external data to recover private user information
such as gender and age. In this paper we show that user vectors calculated by a
common recommender system can be exploited in this way. We propose the
privacy-adversarial framework to eliminate such leakage of private information,
and study the trade-off between recommender performance and leakage both
theoretically and empirically using a benchmark dataset. An advantage of the
proposed method is that it also helps guarantee fairness of results, since all
implicit knowledge of a set of attributes is scrubbed from the representations
used by the model, and thus can't enter into the decision making. We discuss
further applications of this method towards the generation of deeper and more
insightful recommendations.Comment: International Conference on Pattern Recognition and Method
Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations
The work described here was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61373051; the National Science and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory for Symbolic Computation and Knowledge Engineering, Ministry of Education, China, and the UK Economic & Social Research Council (ESRC); award reference: ES/M001628/1.Preprin
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
RNNs have been shown to be excellent models for sequential data and in
particular for data that is generated by users in an session-based manner. The
use of RNNs provides impressive performance benefits over classical methods in
session-based recommendations. In this work we introduce novel ranking loss
functions tailored to RNNs in the recommendation setting. The improved
performance of these losses over alternatives, along with further tricks and
refinements described in this work, allow for an overall improvement of up to
35% in terms of MRR and Recall@20 over previous session-based RNN solutions and
up to 53% over classical collaborative filtering approaches. Unlike data
augmentation-based improvements, our method does not increase training times
significantly. We further demonstrate the performance gain of the RNN over
baselines in an online A/B test.Comment: CIKM'18, authors' versio
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